Author name cluster

Yuliang Ma

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5 papers

2 author rows

IROS Conference 2025 Conference Paper

Deep Learning-based Proactive Hazard Prediction for Human-Robot Collaboration with Sensor Malfunctions

Yuliang Ma
Zilin Jin
Qi Liu
Ilshat Mamaev
Andrey Morozov 0001

Safety is a critical concern in human-robot collaboration (HRC). As collaborative robots take on increasingly complex tasks in human environments, their systems have become more sophisticated through the integration of multimodal sensors, including force-torque sensors, cameras, LiDARs, and IMUs. However, existing studies on HRC safety primarily focus on ensuring safety under normal operating conditions, overlooking scenarios where internal sensor faults occur. While anomaly detection modules can help identify sensor errors and mitigate hazards, two key challenges remain: (1) no anomaly detector is flawless, and (2) not all sensor malfunctions directly threaten human safety. Relying solely on anomaly detection can lead to missed errors or excessive false alarms. To enhance safety in real-world HRC applications, this paper introduces a deep learning-based method that proactively predicts hazards following the detection of sensory anomalies. We simulate two common types of faults—bias and noise—affecting joint sensors and monitor abnormal manipulator behaviors that could pose risks in fenceless HRC environments. A dataset of 2, 400 real-world samples is collected to train the proposed hazard prediction model. The approach leverages multimodal inputs, including RGB-D images, human pose, joint states, and planned robot paths, to assess whether sensor malfunctions could lead to hazardous events. Experimental results show that the proposed method outperforms state-of-the-art models, while offering faster inference speed. Additionally, cross-scenario testing confirms its strong generalization capabilities. The code and datasets are available at: DL-based-Hazard-Prediction.

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AIIM Journal 2025 Journal Article

Lesion boundary detection for skin lesion segmentation based on boundary sensing and CNN-transformer fusion networks

Xuzhen Huang
Yuliang Ma
Xiajin Mei
Zizhuo Wu
Mingxu Sun
Qingshan She

Traditional convolutional neural networks often struggle to capture global information and handle ambiguous boundaries during complex skin lesion segmentation tasks. To tackle this challenge, we proposed MPBA-Net, a hybrid network that integrates multi-pooling fusion and boundary-aware refinement. The network integrated Convolutional Neural Network (CNN) and Transformer to generate rich skin lesion feature maps for comprehensive feature extraction. Specifically, we introduced a boundary-aware attention gate (BAAG) module in the Transformer encoder layer and added a boundary cross attention (BCA) module at the end of the network to capture critical skin lesion boundary features. Additionally, we developed a multi-pooling fusion (MPF) module that extracts global multi-scale features by fusing improved Spatial Pyramid (SP) and Atrous Spatial Pyramid Pooling (ASPP). To optimize training, we designed a Point Loss derived from Binary Cross-Entropy (BCE) and combined it with Dice Loss to form a hybrid loss function. This approach not only enhances classification performance but also provides more precise measurement of the similarity between segmentation results and ground truth annotations. Ablation experiments on the ISIC2018 dataset validated the effectiveness of our fusion strategies and network improvements. Comparative experiments on the ISIC2016, ISIC2017, and ISIC2018 datasets showed that the Dice index of MPBA-Net outperformed other comparative segmentation methods in all three datasets, achieving 91. 47 %, 87. 04 %, and 88. 93 %, respectively. Quantitative and qualitative results demonstrate that our method improves skin lesion segmentation accuracy, aiding dermatologists in clinical diagnosis and treatment. Our code is available at https: //github. com/FengYuchenGuang/MPBA-Net.

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JBHI Journal 2025 Journal Article

LUCF-Net: Lightweight U-Shaped Cascade Fusion Network for Medical Image Segmentation

Qingshan She
Songkai Sun
Yuliang Ma
Rihui Li
Yingchun Zhang

The performance of modern U-shaped neural networks for medical image segmentation has been significantly enhanced by incorporating Transformer layers. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to their high complexity. To address this challenge, we proposed a new lightweight U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It utilized an asymmetrical structural design and incorporated both local and global modules to enhance its capacity for local and global modeling. Additionally, a multi-layer cascade fusion decoding network was designed to further bolster the network's information fusion capabilities. Validation performed on open-source CT, MRI, and dermatology datasets demonstrated that the proposed model outperformed other state-of-the-art methods in handling local-global information, achieving an improvement of 1. 46% in Dice coefficient and 2. 98 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a network that combines Convolutional Neural Network and Transformer architectures, it achieves competitive segmentation performance with only 6. 93 million parameters and 6. 6 gigabytes of floating point operations, without the need for pre-training. In summary, the proposed method demonstrated enhanced performance while retaining a simpler model design compared to other Transformer-based segmentation networks.

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IROS Conference 2024 Conference Paper

Multimodal Failure Prediction for Vision-based Manipulation Tasks with Camera Faults

Yuliang Ma
Jingyi Liu
Ilshat Mamaev
Andrey Morozov 0001

Due to the increasing behavioral and structural complexity of robots, it is challenging to predict the execution outcome after error detection. Anomaly detection methods can help detect errors and prevent potential failures. However, not every fault leads to a failure due to the system’s fault tolerance or unintended error masking. In practical applications, a robotic system should have a potential failure evaluation module to estimate the probability of failures when receiving an error alert. Subsequently, a decision-making mechanism should help to take the next action, e. g. , terminate, degrade performance, or continue the execution of the task. This paper proposes a multimodal method for failure prediction for vision-based manipulation systems that suffer from potential camera faults. We inject faults into images (e. g. , noise and blur) and observe manipulation failure scenarios (e. g. , pick failure, place failure, and collision) that can occur during the task. Through extensive fault injection experiments, we created a FAULT-to-FAILURE dataset containing 4000 real-world manipulation samples. The dataset is subsequently used to train the failure predictor. Our approach processes the combination of RGB images, masked images, and planned paths to effectively evaluate whether a certain faulty image could potentially lead to a manipulation failure. Results demonstrate that the proposed method outperforms state-of-the-art models in terms of overall performance, requires fewer sensors, and achieves faster inference speeds. The analytical software prototype and dataset are available at Github: MultimodalFailurePrediction.

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JBHI Journal 2023 Journal Article

ULFAC-Net: Ultra-Lightweight Fully Asymmetric Convolutional Network for Skin Lesion Segmentation

Yuliang Ma
Liping Wu
Yunyuan Gao
Farong Gao
Jianhai Zhang
Zhizeng Luo

Segmentation of skin lesions is a critical step in the process of skin lesion diagnosis. Such segmentation is challenging due to the irregular shape, fuzzy contours and severe noise interference in the skin lesion region. Existing deep learning-based skin lesion segmentation methods are usually computationally expensive, hindering their deployment in dermoscopic devices with poor computational power. To address these challenges, we propose an ultralightweight fully asymmetric convolutional network for skin lesion segmentation, called ULFAC-Net. we use a parallel asymmetric convolutional (PAC) module to extract features instead of the traditional square convolution, and innovatively propose a PAC module with dual attention (Att-PAC) to enhance the feature representation. Based on the PAC and Att-PAC modules, we further propose a lightweight textual information submodule. To balance the number of parameters and performance of the model, we also hand-design an asymmetric encoder-decoder architecture. In this paper, we validate the effectiveness and robustness of the proposed ULFAC-Net on four publicly available skin lesion segmentation datasets (ISIC2018, ISBI2017, ISIC2016 and PH2 datasets). The experimental results show that ULFAC-Net achieves competitive segmentation performance with only 0. 842 million(0. 842M) parameters and 3. 71 gigabytes of floating point operations (GFLOPs) compared to other state-of-the-art methods.

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