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Yaonan Wang

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19 papers

2 author rows

JBHI Journal 2026 Journal Article

DF-DiffVSR: Deformable Field-Driven Diffusion Model for Inter-Slice Continuity Enhancement in Medical Volume Super-Resolution

Can Wang
Min Liu
Qinghao Liu
Yuehao Zhu
Xiang Chen
Licheng Liu
Yaonan Wang
Erik Meijering

Medical volumetric imaging is crucial for precise diagnosis, but limited by equipment and acquisition constraints, anisotropic resolution leads to challenges in detecting small lesions and 3D visualization. While volumetric super-resolution methods can mitigate this issue, existing techniques suffer from limited receptive fields, failing to fully exploit inter-slice correlations and resulting in compromised inter-slice continuity. To address this limitation, we propose DF-DiffVSR, a novel deformable field-enhanced diffusion model for medical volume super resolution. The proposed method integrates optical flow principles with diffusion models through a Deformable Field Extraction (DFE) module, which explicitly learns inter slice motion information to enhance structural continuity in the through-plane direction. Furthermore, we design a Multiscale Large Kernel Convolution (MLKC) module that employs striped convolutions with varying kernel sizes to expand the receptive field and capture global anatomical context. Evaluated on RPLHR-CT and IXI-T2 datasets, DF DiffVSR achieves state-of-the-art (SOTA) performance, surpassing the sub-optimal method by 0. 732 dB and 0. 214 dB in PSNR, respectively, demonstrating superior capabilities in preserving inter-slice continuity and recovering fine grained details.

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AAAI Conference 2026 Conference Paper

MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging

Shufeng Kong
Zijie Wang
Nuan Cui
Hao Tang
Yihan Meng
Yuanyuan Wei
Feifan Chen
Yingheng Wang

Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. Tongue image diagnosis is a particularly challenging domain that requires fine-grained visual and semantic understanding. Our approach leverages self-supervised masked autoencoder (MAE) to learn transferable visual representations from unlabeled data; employs graph attention networks (GAT) to model label correlations through expert-defined structured graphs; enforces clinical priors via constraint-aware optimization using KL divergence and regularization losses; and mitigates imbalance using asymmetric loss (ASL) and boosting ensembles. To address annotation scarcity, we also introduce TongueAtlas-4K, a comprehensive expert-curated benchmark comprising 4,000 images annotated with 22 diagnostic labels–representing the largest public dataset in tongue analysis. Validation shows our method achieves state-of-the-art performance. While optimized for tongue diagnosis, the framework readily generalizes to broader diagnostic medical imaging tasks.

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AAAI Conference 2026 Conference Paper

Mono3DVG-EnSD: Enhanced Spatial-aware and Dimension-decoupled Text Encoding for Monocular 3D Visual Grounding

Yuzhen Li
Min Liu
Zhaoyang Li
Yuan Bian
Xueping Wang
Erbo Zhai
Yaonan Wang

Monocular 3D Visual Grounding (Mono3DVG) is an emerging task that locates 3D objects in RGB images using text descriptions with geometric cues. However, existing methods face two key limitations. Firstly, they often over-rely on high-certainty keywords that explicitly identify the target object while neglecting critical spatial descriptions. Secondly, generalized textual features contain both 2D and 3D descriptive information, thereby capturing an additional dimension of details compared to singular 2D or 3D visual features. This characteristic leads to cross-dimensional interference when refining visual features under text guidance. To overcome these challenges, we propose Mono3DVG-EnSD, a novel framework that integrates two key components: the CLIP-Guided Lexical Certainty Adapter (CLIP-LCA) and the Dimension-Decoupled Module (D2M). The CLIP-LCA dynamically masks high-certainty keywords while retaining low-certainty implicit spatial descriptions, thereby forcing the model to develop a deeper understanding of spatial relationships in captions for object localization. Meanwhile, the D2M decouples dimension-specific (2D/3D) textual features from generalized textual features to guide corresponding visual features at same dimension, which mitigates cross-dimensional interference by ensuring dimensionally-consistent cross-modal interactions. Through comprehensive comparisons and ablation studies on the Mono3DRefer dataset, our method achieves state-of-the-art (SOTA) performance across all metrics. Notably, it improves the challenging Far(Acc@0.5) scenario by a significant +13.54%.

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IS Journal 2026 Journal Article

Skeleton-Based Traffic Gesture Recognition via a Motion-Guided 2S-GCN in Autonomous Driving

Xiaofeng Guo
Yang Mo
Yaonan Wang
Qing Zhu

For the safety of autonomous driving systems, recognizing the gestures of cyclists and controllers on traffic roads is crucial. We propose a novel two-stream graph convolutional network (2S-GCN) to address these challenges. First, we introduced a motion-guided module connecting the motion and joint stream. By leveraging the motion stream’s spatial expression capability, we guided the joint stream’s learning in spatial dimensions to enhance the network’s performance to short action durations. Second, we utilized a simplified skeleton topology that exclusively captures the upper body during data preprocessing, thereby preserving essential information while eliminating redundancy. This approach enhances the network’s adaptability to atypical action postures. Third, we use a GCN embedded with a multichannel attention module as the backbone, which is particularly suitable for small datasets, and conduct extensive experiments on two datasets. The experimental results demonstrate that our network can accurately recognize traffic gestures and has significant advantages over state-of-the-art methods.

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TIST Journal 2026 Journal Article

You Can Only Tune Normalization: A Simple and Effective Approach to Parameter-Efficient Fine-Tuning

Lingyun Huang
JianXu Mao
Junfei Yi
Ziming Tao
Ziyang Peng
Wei He
Rui Liu
Yaonan Wang

To tackle the issue of excessive parameter volumes during fine-tuning of large-scale pre-trained models with full parameters, Parameter-Efficient Fine-Tuning (PEFT) methods have been introduced. The core concept involves freezing the backbone network of the model and updating only a small subset of parameters. This strategy not only decreases the number of parameters needed for training but also delivers performance comparable to Full-Tuning, even surpassing it on certain datasets. However, most popular PEFT methods introduce extra parameters or modules for fine-tuning, which come with inherent limitations. In response, we propose a straightforward and efficient PEFT method called You Can Only Tune Normalization (YONO). YONO focuses solely on tuning the normalization layer and the final classification layer of the model. This method avoids adding extra modules, making it easily applicable to any model without causing inference delays. We extensively tested YONO on 28 benchmark datasets, and the results indicate that it requires significantly fewer parameters compared to other advanced PEFT methods. Additionally, we validated YONO’s efficiency and generalizability across various vision models. Finally, we further explore the essence of PEFT methods, whether they learn new knowledge or expose the capabilities that a model has already learned. Our findings suggest that YONO is more sensitive to improvements in dataset quality, making it a promising candidate for future scaling to larger models.

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IROS Conference 2025 Conference Paper

Decentralized Multi-robot Navigation Policy with Enhanced Security Using Graph GRU Policy Network

Lin Chen
Yuxuan Ao
Zhen Zhou
Yaonan Wang
Danwei Wang

Formulating a multi-robot obstacle avoidance policy is essential for enabling safe and efficient navigation in multi-robot environments, forming a critical component of the effective operation of multi-robot systems. Recently, reinforcement learning has been applied to improve the performance of decentralized, policy-driven robots in task execution. However, ensuring the safety of these agents during movement remains a significant challenge due to the inherent risks associated with the reinforcement learning process, such as frequent collisions. To address this issue and enhance the safety of policy-guided multi-robot navigation, we propose a novel policy based on imitation learning. This framework introduces a novel policy neural network that integrates a graph attention mechanism with the GRU network structure. The key innovation lies in utilizing the interactions between neighboring robots to enhance the safety of their movements. In a multi-robot simulation environment, robot behaviors are directed by the proposed policy. A comparative analysis was conducted between our approach and RL-RVO, one of the advanced methods in the field. The results demonstrate that our approach outperforms RL-RVO, achieving a higher success rate and significantly improving safety performance.

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IROS Conference 2025 Conference Paper

Parameterized Motion Planning for Aerial Manipulators in Contact with Unstructured Surfaces

Zhixing Zhang
Hang Zhong
Chaoquan Lin
Weizheng Wang
Hean Hua
Hui Zhang
Yaonan Wang

Motion planning for continuous contact-based aerial manipulators on complex unstructured surfaces remains a substantial challenge due to the sophisticated topology of unstructured surfaces. While direct planning in the high-dimensional configuration space manifolds faces efficiency limitations, simplified planning in the parametric space sacrifices trajectory quality. Therefore, this paper proposes a sampling-based motion planning method, namely, parameter-configuration space fast marching tree (PCS-FMT*), which integrates both configuration and parameter space information. The proposed PCS-FMT* introduces a reparameterization strategy that compresses the planning space into a low-dimensional parameter manifold while preserving metric consistency with the original configuration space. Thus, PCS-FMT* can efficiently plan in the parameter space and optimize the motion trajectory. Simulations on challenging unstructured surfaces validate the effectiveness of PCS-FMT* for aerial manipulators in contact with unstructured surfaces.

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NeurIPS Conference 2025 Conference Paper

Searching Efficient Semantic Segmentation Architectures via Dynamic Path Selection

Yuxi Liu
Min Liu
Shuai Jiang
Yi Tang
Yaonan Wang

Existing NAS methods for semantic segmentation typically apply uniform optimization to all candidate networks (paths) within a one-shot supernet. However, the concurrent existence of both promising and suboptimal paths often results in inefficient weight updates and gradient conflicts. This issue is particularly severe in semantic segmentation due to its complex multi-branch architectures and large search space, which further degrade the supernet's ability to accurately evaluate individual paths and identify high-quality candidates. To address this issue, we propose Dynamic Path Selection (DPS), a selective training strategy that leverages multiple performance proxies to guide path optimization. DPS follows a stage-wise paradigm, where each phase emphasizes a different objective: early stages prioritize convergence, the middle stage focuses on expressiveness, and the final stage emphasizes a balanced combination of expressiveness and generalization. At each stage, paths are selected based on these criteria, concentrating optimization efforts on promising paths, thus facilitating targeted and efficient model updates. Additionally, DPS integrates a dynamic stage scheduler and a diversity-driven exploration strategy, which jointly enable adaptive stage transitions and maintain structural diversity among selected paths. Extensive experiments demonstrate that, under the same search space, DPS can discover efficient models with strong generalization and superior performance.

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AAAI Conference 2025 Conference Paper

Semantic Ambiguity Modeling and Propagation for Fine-Grained Visual Cross View Geo-Localization

Mingtao Feng
Fenghao Tian
Jianqiao Luo
Zijie Wu
Weisheng Dong
Yaonan Wang
Ajmal Saeed Mian

Visual cross view geo-localization is generally approached within a joint retrieval-and-calibration framework. However, existing methods overlook semantic ambiguities arising from query and reference images characterized by low overlap, dynamic foregrounds, viewpoint changes, and perceptual aliasing. This makes it challenging to automatically control the relative importance of the two tasks, potentially compromising the retrieval task in favor of the offset regression. Consequently, the model may encounter conflicting dominating gradients during joint training. To address this, we propose to model the semantic ambiguity during the offset regression process by integrating associated uncertainty scores, represented as 2D Gaussian distributions, to mitigate negative transfer effects within the joint tasks. We further introduce an uncertainty-aware similarity metric to enhance similarity assessment between query and reference images, accounting for their semantic ambiguities. This metric propagates uncertainty scores into the retrieval task, focusing on certain samples and learning discriminative feature embeddings, allowing the model to adaptively handle conflicting dominating gradients during joint training. Extensive experiments demonstrate that our method improves the overall performance of the joint tasks, achieving state-of-the-art results on the VIGOR and CVACT datasets.

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AAMAS Conference 2025 Conference Paper

Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning

Lunjun Liu
Weilai Jiang
Yaonan Wang

In multi-agent reinforcement learning (MARL), the centralized training with decentralized execution (CTDE) framework has gained widespread adoption due to its strong performance. However, the further development of CTDE faces two key challenges. First, agents struggle to autonomously assess the relevance of input information for cooperative tasks, impairing their decision-making abilities. Second, in communication-limited scenarios with partial observability, agents are unable to access global information, restricting their ability to collaborate effectively from a global perspective. To address these challenges, we introduce a novel cooperative MARL framework based on information selection and tacit learning. In this framework, agents gradually develop implicit coordination during training, enabling them to infer the cooperative behavior of others in a discrete space without communication, relying solely on local information. Moreover, we integrate gating and selection mechanisms, allowing agents to adaptively filter information based on environmental changes, thereby enhancing their decision-making capabilities. Experiments on popular MARL benchmarks show that our framework can be seamlessly integrated with state-of-the-art algorithms, leading to significant performance improvements.

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EAAI Journal 2023 Journal Article

Separable-programming based probabilistic-iteration and restriction-resolving correlation filter for robust real-time visual tracking

Baiheng Cao
Xuedong Wu
JianXu Mao
Yaonan Wang
Zhiyu Zhu

Visual tracking methods based on correlation filter (CF) are to estimate the location and the scale of tracking object within video sequences. However, there are still several inadequate hypotheses within the CF framework. These inadequate hypotheses include the constant Gaussian label map, the predetermined object location and the insufficient utilization for features. To further address the problems caused by these hypotheses, this paper proposes a novel separable-programming based probabilistic-iteration and restriction-resolving correlation filter (PRCF). The main innovation points of this work are: 1) the separable-programming based tracking framework is adopted to achieve adaptive regulation incorporation and to simultaneously address location prediction and filter training; 2) the probabilistic-iteration scheme is suggested to update the Gaussian label for each frame to solve the problem of ad-hoc label map; 3) the adaptive feature fusion measure is introduced to abate the effects of insufficient utilization for numerous features; 4) the convergence behavior of PRCF is proved and discussed through theoretical analysis and worst-case convergence rate calculation. Experiments have also been conducted to test the efficiency of suggested PRCF on 7 benchmarks: OTB100, TC128, VOT2016, VOT2019, UAV123, NFS and LaSOT. The results have indicated that: 1) the PRCF obtains favorable performances compared with other 15 state-of-the-art (SOTA) CF-based trackers; 2) the PRCF reports comparable results against 4 deep learning based trackers; 3) the PRCF achieves a real-time speed of 52 frames-pre-second (FPS) on 7 benchmarks averagely. Thus, the PRCF is qualified for practical target tracking scenarios such as video surveillance and unmanned aerial vehicles (UAVs).

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JBHI Journal 2022 Journal Article

An O-Shape Neural Network With Attention Modules to Detect Junctions in Biomedical Images Without Segmentation

Yuqiang Zhang
Min Liu
Fuhao Yu
Tieyong Zeng
Yaonan Wang

Junction plays an important role in biomedical research such as retinal biometric identification, retinal image registration, eye-related disease diagnosis and neuron reconstruction. However, junction detection in original biomedical images is extremely challenging. For example, retinal images contain many tiny blood vessels with complicated structures and low contrast, which makes it challenging to detect junctions. In this paper, we propose an O-shape Network architecture with Attention modules (Attention O-Net), which includes Junction Detection Branch (JDB) and Local Enhancement Branch (LEB) to detect junctions in biomedical images without segmentation. In JDB, the heatmap indicating the probabilities of junctions is estimated and followed by choosing the positions with the local highest value as the junctions, whereas it is challenging to detect junctions when the images contain weak filament signals. Therefore, LEB is constructed to enhance the thin branch foreground and make the network pay more attention to the regions with low contrast, which is helpful to alleviate the imbalance of the foreground between thin and thick branches and to detect the junctions of the thin branch. Furthermore, attention modules are utilized to introduce the feature maps of LEB to JDB, which can establish a complementary relationship and further integrate local features and contextual information between these two branches. The proposed method achieves the highest average F1-scores of 0. 82, 0. 73 and 0. 94 in two retinal datasets and one neuron dataset, respectively. The experimental results confirm that Attention O-Net outperforms other state-of-the-art detection methods, and is helpful for retinal biometric identification.

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JBHI Journal 2022 Journal Article

DeepRayburst for Automatic Shape Analysis of Tree-Like Structures in Biomedical Images

Yi Jiang
Weixun Chen
Min Liu
Yaonan Wang
Erik Meijering

Precise quantification of tree-like structures from biomedical images, such as neuronal shape reconstruction and retinal blood vessel caliber estimation, is increasingly important in understanding normal function and pathologic processes in biology. Some handcrafted methods have been proposed for this purpose in recent years. However, they are designed only for a specific application. In this paper, we propose a shape analysis algorithm, DeepRayburst, that can be applied to many different applications based on a Multi-Feature Rayburst Sampling (MFRS) and a Dual Channel Temporal Convolutional Network (DC-TCN). Specifically, we first generate a Rayburst Sampling (RS) core containing a set of multidirectional rays. Then the MFRS is designed by extending each ray of the RS to multiple parallel rays which extract a set of feature sequences. A Gaussian kernel is then used to fuse these feature sequences and outputs one feature sequence. Furthermore, we design a DC-TCN to make the rays terminate on the surface of tree-like structures according to the fused feature sequence. Finally, by analyzing the distribution patterns of the terminated rays, the algorithm can serve multiple shape analysis applications of tree-like structures. Experiments on three different applications, including soma shape reconstruction, neuronal shape reconstruction, and vessel caliber estimation, confirm that the proposed method outperforms other state-of-the-art shape analysis methods, which demonstrate its flexibility and robustness.

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EAAI Journal 2022 Journal Article

Review on the COVID-19 pandemic prevention and control system based on AI

Junfei Yi
Hui Zhang
JianXu Mao
Yurong Chen
Hang Zhong
Yaonan Wang

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people’s lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic. To respond scientifically to major emergencies like COVID-19, this article reviews the use of artificial intelligence in the combat against the pandemic from COVID-19 large data, intelligent devices and systems, and intelligent robots. This article’s primary contributions are in two aspects: (1) we summarized the applications of AI in the pandemic, including virus spreading prediction, patient diagnosis, vaccine development, excluding potential virus carriers, telemedicine service, economic recovery, material distribution, disinfection, and health care. (2) We concluded the faced challenges during the AI-based pandemic prevention process, including multidimensional data, sub-intelligent algorithms, and unsystematic, and discussed corresponding solutions, such as 5G, cloud computing, and unsupervised learning algorithms. This article systematically surveyed the applications and challenges of AI technology during the pandemic, which is of great significance to promote the development of AI technology and can serve as a new reference for future emergencies.

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JBHI Journal 2021 Journal Article

Efficient 3D Junction Detection in Biomedical Images Based on a Circular Sampling Model and Reverse Mapping

Lan Shen
Min Liu
Chao Wang
Changhao Guo
Erik Meijering
Yaonan Wang

Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87. 11% and 88. 33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.

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JBHI Journal 2021 Journal Article

Neuron Image Segmentation via Learning Deep Features and Enhancing Weak Neuronal Structures

Bo Yang
Weixun Chen
Huiqiong Luo
Yinghui Tan
Min Liu
Yaonan Wang

Neuron morphology reconstruction (tracing) in 3D volumetric images is critical for neuronal research. However, most existing neuron tracing methods are not applicable in challenging datasets where the neuron images are contaminated by noises or containing weak filament signals. In this paper, we present a two-stage 3D neuron segmentation approach via learning deep features and enhancing weak neuronal structures, to reduce the impact of image noise in the data and enhance the weak-signal neuronal structures. In the first stage, we train a voxel-wise multi-level fully convolutional network (FCN), which specializes in learning deep features, to obtain the 3D neuron image segmentation maps in an end-to-end manner. In the second stage, a ray-shooting model is employed to detect the discontinued segments in segmentation results of the first-stage, and the local neuron diameter of the broken point is estimated and direction of the filamentary fragment is detected by rayburst sampling algorithm. Then, a Hessian-repair model is built to repair the broken structures, by enhancing weak neuronal structures in a fibrous structure determined by the estimated local neuron diameter and the filamentary fragment direction. Experimental results demonstrate that our proposed segmentation approach achieves better segmentation performance than other state-of-the-art methods for 3D neuron segmentation. Compared with the neuron reconstruction results on the segmented images produced by other segmentation methods, the proposed approach gains 47. 83% and 34. 83% improvement in the average distance scores. The average Precision and Recall rates of the branch point detection with our proposed method are 38. 74% and 22. 53% higher than the detection results without segmentation.

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JBHI Journal 2019 Journal Article

Weakly Supervised Biomedical Image Segmentation by Reiterative Learning

Qiaokang Liang
Yang Nan
Gianmarc Coppola
Kunglin Zou
Wei Sun
Dan Zhang
Yaonan Wang
Guanzhen Yu

Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as overlapped region forecast, for the automatic segmentation of gastric cancer images. To the best of our knowledge, this report for the first time describes that deep learning has been applied to the segmentation of gastric cancer images. Moreover, a reiterative learning framework that achieves superior performance without pretraining or further manual annotation is presented to train a simple network on weakly annotated biomedical images. We customize the loss function to make the model converge faster while avoiding becoming trapped in local minima. Patch boundary errors were eliminated by our overlapped region forecast algorithm. By studying the characteristics of the model trained using two different patch extraction methods, we train iteratively and integrate predictions and weak annotations to improve the quality of the training data. Using these methods, a mean Intersection over Union coefficient of 0. 883 and a mean accuracy of 91. 09% were achieved on the partially labeled dataset, thereby securing a win in the 2017 China Big Data and Artificial Intelligence Innovation and Entrepreneurship Competition.

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EAAI Journal 2014 Journal Article

Adaptive motion/force control strategy for non-holonomic mobile manipulator robot using recurrent fuzzy wavelet neural networks

Yaonan Wang
ThangLong Mai
JianXu Mao

In our study, we develop an adaptive position tracking system and a force control strategy for non-holonomic mobile manipulator robot, which combine the merits of Recurrent Fuzzy Wavelet Neural Networks (RFWNNs). In order to deal with the unknown knowledge problems of the robotic system, an adaptive RFWNNs control scheme with the dynamic structure and online learning ability is utilized to approximate unknown dynamics without the requirement of prior controlled system information. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, disturbances. According to the adaptive position tracking control design, an adaptive robust controller is also considered for the non-holonomic constraint force. The design of the adaptive online learning algorithms is derived by using the Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability but also the tracking performance of the mobile manipulator robot control system. The effectiveness and robustness of the proposed method are demonstrated by comparative simulation and experimental results that are implemented in an indoor cleaning crawler-type mobile manipulator robot system.

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EAAI Journal 2011 Journal Article

Intelligent injection liquid particle inspection machine based on two-dimensional Tsallis Entropy with modified pulse-coupled neural networks

Yaonan Wang
Ji Ge
Hui Zhang
Bowen Zhou

The Automatic Liquid Particle Inspection Machine (AIM) using 2-D Tsallis Entropy with modified pulse-coupled neural networks (PCNN) is used in order to detect visible foreign particles within injection fluids. According to the motion of the particles in liquid, appropriate mechanisms are utilized which guarantees that the inspection machine will follow detection procedures: “Rotation, Abruptly Braking, Video Tracking” to extract tiny objects from complicated sequential images. In order to reduce the influence derived from air bubbles, improved spin/stop techniques are applied. The external capture mode of CCD cameras is used to avoid the possibility of omitting certain particles by trivial displacement. 2-D Tsallis Entropy with modified PCNN is applied in order to segment the difference images, and then to judge the existence of foreign particles according to the continuity and smoothness of their traces. Preliminary experimental results (125ml 0. 9% sodium chloride solution and 10% glucose as the samples) indicate that the inspection machine, which is superior to proficient inspectors, can detect the visible foreign particles effectively and that this detection speed and accuracy, as well as the correct detection rate can also facilitate the medicinal construction.

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