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Rong Hu

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7 papers

2 author rows

EAAI Journal 2025 Journal Article

Enhanced Cross Layer Refinement Network for robust lane detection across diverse lighting and road conditions

Weilong Dai
Zuoyong Li
Xiaofeng Xu
Xiaobo Chen
Huanqiang Zeng
Rong Hu

With the rapid development of autonomous driving technology, lane detection, a key component of intelligent vehicle systems, is crucial for ensuring road safety and efficient vehicle navigation. In this paper, a new lane detection method is proposed to address the problem of degraded performance of existing lane detection methods when dealing with complex road environments. The proposed method evolves from the original Cross Layer Refinement Network (CLRNet) by incorporating two of our carefully designed core components: the Global Feature Optimizer (GFO) and the Adaptive Lane Geometry Aggregator (ALGA). The GFO is a multi-scale attention mechanism that mimics the human visual focusing ability, effectively filtering out unimportant information and focusing on the image regions most relevant to the task. The ALGA is a shape feature-aware aggregation module that utilizes the shape prior of lanes to enhance the correlation of anchor points in an image, better fusing global and local information. By integrating both components into CLRNet, an enhanced version called Enhanced CLRNet (E-CLRNet) is presented, which exhibits higher performance stability in complex roadway scenarios. Experiments on the CULane dataset reveal that E-CLRNet demonstrates superior performance stability over the original CLRNet in complex scenarios, including curves, shadows, missing lines, and dazzling light conditions. In particular, in the curves, the F1 score of E-CLRNet is improved by almost 3% over the original CLRNet. This study not only improves the accuracy and performance stability of lane detection but also provides a new solution for the application of autonomous driving technology in complex environments, which promotes the development of intelligent vehicle systems.

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EAAI Journal 2025 Journal Article

Phased Noise Enhanced Multiple Feature Discrimination Network for fabric defect detection

Haoran Ma
Zuoyong Li
Haoyi Fan
Xiangpan Zheng
Jiaquan Yan
Rong Hu

Fabric defect detection is crucial for evaluating the quality of textile products. However, the subtlety and scarcity of fabric defects pose challenges to the task of detecting. Therefore, we propose a Phased Noise Enhanced Multiple Feature Discrimination Network, which is based on phased noise enhancement strategy and multiple feature discrimination module to improve the model’s ability to identify complex and subtle flaws. Specifically, we propose the phased noise enhancement strategy in the feature space to simulate feature-level anomalies that are closer to reality. This strategy can improve the input quality of the feature reconstructor, so that helps its perception and reconstruction ability. Then, we propose the multiple feature discrimination module, which has dual feature branches to improve its ability to distinguish more complex detailed texture features. In addition, we propose a subsampling module to reduce feature redundancy and ensure efficient inference speed. Finally, we conduct extensive experiments and ablation studies on two publicly available fabric datasets, AITEX and Kaggle Fabric. The experimental results show that the proposed method achieved 92% and 100% image level metrics and 97. 5% and 67. 1% pixel level metrics on two datasets, respectively, which is superior to the current state-of-the-art methods. In addition, our method also demonstrated significant performance in generalization experiments.

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EAAI Journal 2024 Journal Article

A multidimensional probabilistic model based evolutionary algorithm for the energy-efficient distributed flexible job-shop scheduling problem

Zi-Qi Zhang
Ying Li
Bin Qian
Rong Hu
Jian-Bo Yang

With escalating environmental effects, the spotlight on low-carbon manufacturing has garnered significant attention. The rise of distributed production has emerged as a prominent trend in response to the imperatives of economic globalization. This article focuses on addressing the energy-efficient distributed flexible job-shop scheduling problem (EE_DFJSP), with the aim of minimizing both makespan and total energy consumption (TEC) simultaneously. The production process contains four pivotal phases: 1) job assignment in distributed factories; 2) machine selection within factories; 3) operation allocation on flexible machines; and 4) machine speed adjustment for processing. Given the problem's multi-phase and strong coupling characteristics, it is imperative to develop a promising evolutionary algorithm (EA) for EE_DFJSP. To tackle this challenge, we propose a multidimensional probabilistic model-based EA (MPMEA) paradigm. First, problem-specific encoding and decoding schemes are developed based on the solution features of EE_DFJSP. Second, a hybrid initialization strategy incorporating four heuristic rules is devised to yield an initial population with diversity. Third, an effective union probabilistic model (UPM) is formulated to learn promising patterns from superior solutions, and an efficient sampling strategy is designed to produce high-quality offspring individuals. To achieve a balance between global exploration and local exploitation, problem-specific multiple neighborhood operators are proposed to perform an in-depth local search. Furthermore, a two-stage energy-saving speed adjustment strategy is designed for the superior solutions obtained through local search. Finally, computational comparisons and simulation studies are conducted to validate the effectiveness and superiority of the MPMEA in effectively addressing EE_DFJSP.

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YNIMG Journal 2023 Journal Article

Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet

Ping Hu
Haizhu Zhou
Tengfeng Yan
Hongping Miu
Feng Xiao
Xinyi Zhu
Lei Shu
Shuang Yang

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

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AAAI Conference 2023 Conference Paper

SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition

Rong Hu
Ling Chen
Shenghuan Miao
Xing Tang

In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity. Existing UDA models usually align samples across domains without differentiation, which ignores the difference among samples. In this paper, we propose an unsupervised domain adaptation model with sample weight learning (SWL-Adapt) for cross-user WHAR. SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network. We introduce the meta-optimization based update rule to learn this network end-to-end, which is guided by meta-classification loss on the selected pseudo-labeled target samples. Therefore, this network can fit a weighting function according to the cross-user WHAR task at hand, which is superior to existing sample differentiation rules fixed for special scenarios. Extensive experiments on three public WHAR datasets demonstrate that SWL-Adapt achieves the state-of-the-art performance on the cross-user WHAR task, outperforming the best baseline by an average of 3.1% and 5.3% in accuracy and macro F1 score, respectively.

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AAAI Conference 2008 Conference Paper

Mining Translations of Web Queries from Web Click-through Data

Rong Hu
Jian Hu
Zheng Chen

Query translation for Cross-Lingual Information Retrieval (CLIR) has gained increasing attention in the research area. Previous work mainly used machine translation systems, bilingual dictionaries, or web corpora to perform query translation. However, most of these approaches require either expensive language resources or complex language models, and cannot achieve timely translation for new queries. In this paper, we propose a novel solution to automatically acquire query translation pairs from the knowledge hidden in the click-through data, that are represented by the URL a user clicks after submitting a query to a search engine. Our proposed solution consists of two stages: identifying bilingual URL pair patterns in the click-through data and matching query translation pairs based on user click behavior. Experimental results on a real dataset show that our method not only generates existing query translation pairs with high precision, but also generates many timely query translation pairs that could not be obtained by previous methods. A comparative study between our system and two commercial online translation systems shows the advantage of our proposed method.

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ICRA Conference 2001 Conference Paper

Camera Self-Calibration from Ellipse Correspondences

Qiang Ji
Rong Hu

We introduce a new technique for camera self-calibration using ellipse correspondences. Based on an analysis of ellipse matches between the images obtained from the same viewpoint but with different and unknown view directions, our approach estimates the intrinsic camera parameters. We present both linear and nonlinear solutions to recovering intrinsic camera parameters. The algorithm's performance is validated extensively using both synthetic and real image data. Compared with similar techniques but using points, we observe a comparable performance. The use of ellipses, however, greatly simplifies feature matching between images, improves matching accuracy, and avoids mismatch.

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