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Prior Distribution and perception Radius Are Important: Learning 3-D Object Detector for Autonomous Driving

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Tao, Ziming Mao, Jianxu Peng, Weixing Wang, Yaonan Yi, Junfei Zhang, Hui Hunan University National Engineering Laboratory of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Robotics Hunan Changsha410082 China

This article proposes an efficient 3-D object detection algorithm for LiDAR point clouds in the field of autonomous driving. Currently, despite significant performance improvements in 3-D LiDAR object detection methods, there are still problems that have impact on 3-D detection accuracy. Typically, since the sampling process and grouping process introduced background points, the final regression step in the detection task would be greatly affected. To address this problem, a method named PR-SSD is proposed to retain more important foreground points during the training pipeline, improving detection accuracy. Specifically, the proposed approach utilizes prior information to guide semantic sampling, referred to as prior distributed semantic sampling (PDSS). This mechanism encourages the network to prioritize foreground targets in regression. In addition, a module named radius-aware attention multiscale grouping (RAMSG) is designed to dynamically reassign grouping weights for multiscale features. In other words, the network has adaptive detection capabilities for targets of different scales. Furthermore, PR-SSD is a single-stage detector which can be trained end-to-end. Finally, extensive experiments and evaluations on large-scale benchmarks KITTI demonstrate that our proposed method significantly enhanced 3-D object detector. In KITTI testing set, PR-SSD achieves 89.69%, 45.08%, and 80.01% detection performance for cars, pedestrians, and cyclists. © 1963-2012 IEEE.

关键词： Vehicle detection

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One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes

arXiv

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arXiv 2025年

作者： Jia, Wanjun Yang, Fan Duan, Mengfei Chen, Xianchi Wang, Yinxi Jiang, Yiming Chen, Wenrui Yang, Kailun Li, Zhiyong School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

Deformable object manipulation in robotics presents significant challenges due to uncertainties in component properties, diverse configurations, visual interference, and ambiguous prompts. These factors complicate both perception and control tasks. To address these challenges, we propose a novel method for One-Shot Affordance Grounding of Deformable Objects (OS-AGDO) in egocentric organizing scenes, enabling robots to recognize previously unseen deformable objects with varying colors and shapes using minimal samples. Specifically, we first introduce the Deformable Object Semantic Enhancement Module (DefoSEM), which enhances hierarchical understanding of the internal structure and improves the ability to accurately identify local features, even under conditions of weak component information. Next, we propose the ORB-Enhanced Keypoint Fusion Module (OEKFM), which optimizes feature extraction of key components by leveraging geometric constraints and improves adaptability to diversity and visual interference. Additionally, we propose an instance-conditional prompt based on image data and task context, effectively mitigates the issue of region ambiguity caused by prompt words. To validate these methods, we construct a diverse real-world dataset, AGDDO15, which includes 15 common types of deformable objects and their associated organizational actions. Experimental results demonstrate that our approach significantly outperforms state-of-the-art methods, achieving improvements of 6.2%, 3.2%, and 2.9% in KLD, SIM, and NSS metrics, respectively, while exhibiting high generalization performance. Source code and benchmark dataset will be publicly available at https://***/Dikay1/OS-AGDO. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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DP-PSSI: Data-Driven Prior for Pharmaceutical Snapshot Spectral Imaging

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Su, Xuesan Mao, Jianxu Wang, Yaonan Zhang, Hui Chen, Yurong Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology Changsha 410082 China

Hyperspectral images contain rich spatial and spectral information, leading to wide applications in many scenarios. Especially in the pharmaceutical field, high spatial and spectral resolution imaging of Active Pharmaceutical Ingredient (API) has an important impact on drug quality detection and analysis. However, existing snapshot computational imaging methods are rarely used in private scenarios, and it is difficult for existing methods to effectively improve the spatial and spectral resolution of API. Therefore, this paper proposes a data-driven pharmaceutical hyperspectral image prior based on Coded Aperture Snapshot Spectral Imager (CASSI) technology. This prior integrates the characteristics of low rank and non-local self-similarity in hyperspectral depth semantic information. And we propose a dense block network in the spatial feature extraction module of Unet which greatly strengthens the feature propagation in solving the problem of extracting detailed texture features from the spatial information of API. At the same time, this method improves the spectral fidelity of the reconstructed data and is conducive to improving the spectral analysis capabilities of API. Our proposed method improves the Peak Signal-to-Noise Ratio (PSNR) by 5.57% and the Structural Similarity Index Measure (SSIM) by 10.99% compared with SOTA method. Furthermore, we designed a prototype of the pharmaceutical snapshot spectral imager to facilitate the experiments, and our API imaging experiments on COVID-19 therapeutic drugs demonstrated the superiority of our method. © 2017 IEEE.

关键词： Computational hyperspectral imaging data-driven prior pharmaceutical snapshot spectral imaging

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Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance

arXiv

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arXiv 2025年

作者： Zhao, Jiayi Teng, Fei Luo, Kai Zhao, Guoqiang Li, Zhiyong Zheng, Xu Yang, Kailun The School of Robotics Hunan University China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China The AI Thrust Hong Kong University of Science and Technology Guangzhou China

The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datasets, demonstrates strong perception potential in perception tasks, its inherent training paradigm prevents it from being suitable for RGB-T tasks. To address these challenges, we propose SHIFNet, a novel SAM2-driven Hybrid Interaction Paradigm that unlocks the potential of SAM2 with linguistic guidance for efficient RGB-Thermal perception. Our framework consists of two key components: (1) Semantic-Aware Cross-modal Fusion (SACF) module that dynamically balances modality contributions through text-guided affinity learning, overcoming SAM2’s inherent RGB bias;(2) Heterogeneous Prompting Decoder (HPD) that enhances global semantic information through a semantic enhancement module and then combined with category embeddings to amplify cross-modal semantic consistency. With 32.27M trainable parameters, SHIFNet achieves state-of-the-art segmentation performance on public benchmarks, reaching 89.8% on PST900 and 67.8% on FMB, respectively. The framework facilitates the adaptation of pre-trained large models to RGB-T segmentation tasks, effectively mitigating the high costs associated with data collection while endowing robotic systems with comprehensive perception capabilities. The source code will be made publicly available at https://***/iAsakiT3T/SHIFNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Multi-Keypoint Affordance Representation for Functional Dexterous Grasping

arXiv

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arXiv 2025年

作者： Yang, Fan Luo, Dongsheng Chen, Wenrui Lin, Jiacheng Cai, Junjie Yang, Kailun Li, Zhiyong Wang, Yaonan School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China College of Computer Science and Electronic Engineering Hunan University Changsha China

Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions and cannot directly constrain the grasping posture, leading to a disconnection between visual perception and manipulation. To address this issue, we propose a multi-keypoint affordance representation for functional dexterous grasping, which directly encodes task-driven grasp configurations by localizing functional contact points. Our method introduces Contact-guided Multi-Keypoint Affordance (CMKA), leveraging human grasping experience images for weak supervision combined with Large Vision Models for fine affordance feature extraction, achieving generalization while avoiding manual keypoint annotations. Additionally, we present a Keypoint-based Grasp matrix Transformation (KGT) method, ensuring spatial consistency between hand keypoints and object contact points, thus providing a direct link between visual perception and dexterous grasping actions. Experiments on public real-world FAH datasets, IsaacGym simulation, and challenging robotic tasks demonstrate that our method significantly improves affordance localization accuracy, grasp consistency, and generalization to unseen tools and tasks, bridging the gap between visual affordance learning and dexterous robotic manipulation. The source code and demo videos will be publicly available at https://***/PopeyePxx/MKA. Copyright © 2025, The Authors. All rights reserved.

关键词： Vision

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EgoEvGesture: Gesture Recognition Based on Egocentric Event Camera

arXiv

引用

arXiv 2025年

作者： Wang, Luming Shi, Hao Yin, Xiaoting Yang, Kailun Wang, Kaiwei Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

Egocentric gesture recognition is a pivotal technology for enhancing natural human-computer interaction, yet traditional RGB-based solutions suffer from motion blur and illumination variations in dynamic scenarios. While event cameras show distinct advantages in handling high dynamic range with ultra-low power consumption, existing RGB-based architectures face inherent limitations in processing asynchronous event streams due to their synchronous frame-based nature. Moreover, from an egocentric perspective, event cameras record data that includes events generated by both head movements and hand gestures, thereby increasing the complexity of gesture recognition. To address this, we propose a novel network architecture specifically designed for event data processing, incorporating (1) a lightweight CNN with asymmetric depthwise convolutions to reduce parameters while preserving spatiotemporal features, (2) a plug-and-play state-space model as context block that decouples head movement noise from gesture dynamics, and (3) a parameter-free Bins-Temporal Shift Module (BSTM) that shifts features along bins and temporal dimensions to fuse sparse events efficiently. We further establish the EgoEvGesture dataset, the first large-scale dataset for egocentric gesture recognition using event cameras. Experimental results demonstrate that our method achieves 62.7% accuracy tested on unseen subjects with only 7M parameters, 3.1% higher than state-of-the-art approaches. Notable misclassifications in freestyle motions stem from high interpersonal variability and unseen test patterns differing from training data. Moreover, our approach achieved a remarkable accuracy of 97.0% on the DVS128 Gesture, demonstrating the effectiveness and generalization capability of our method on public datasets. The dataset and models are made available at https://***/3190105222/EgoEv_Gesture. Copyright © 2025, The Authors. All rights reserved.

关键词： Gesture recognition

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Exploring Video-Based Driver Activity Recognition under Noisy Labels

arXiv

引用

arXiv 2025年

作者： Fan, Linjuan Wen, Di Peng, Kunyu Yang, Kailun Zhang, Jiaming Liu, Ruiping Chen, Yufan Zheng, Junwei Wu, Jiamin Han, Xudong Stiefelhagen, Rainer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai AI Lab China University of Sussex United Kingdom

As an open research topic in the field of deep learning, learning with noisy labels has attracted much attention and grown rapidly over the past ten years. Learning with label noise is crucial for driver distraction behavior recognition, as real-world video data often contains mislabeled samples, impacting model reliability and performance. However, label noise learning is barely explored in the driver activity recognition field. In this paper, we propose the first label noise learning approach for the driver activity recognition task. Based on the cluster assumption, we initially enable the model to learn clustering-friendly low-dimensional representations from given videos and assign the resultant embeddings into clusters. We subsequently perform co-refinement within each cluster to smooth the classifier outputs. Furthermore, we propose a flexible sample selection strategy that combines two selection criteria without relying on any hyperparameters to filter clean samples from the training dataset. We also incorporate a self-adaptive parameter into the sample selection process to enforce balancing across classes. A comprehensive variety of experiments on the public Drive&Act dataset for all granularity levels demonstrates the superior performance of our method in comparison with other label-denoising methods derived from the image classification field. The source code is available at https://***/ilonafan/DAR-noisy-labels. Copyright © 2025, The Authors. All rights reserved.

关键词： Object recognition

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Learning to Learn Transferable Generative Attack for Person Re-Identification

引用

IEEE Transactions on Image Processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts

arXiv

引用

arXiv 2025年

作者： Huang, Yizhou Yang, Fan Zhu, Guoliang Li, Gen Shi, Hao Zuo, Yukun Chen, Wenrui Li, Zhiyong Yang, Kailun School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China School of Informatics University of Edinburgh United Kingdom State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China

Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions. This information is rich and multimodal in nature. Existing multimodal affordance methods face limitations in extracting useful information, mainly due to simple structural designs, basic fusion methods, and large model parameters, making it difficult to meet the performance requirements for practical deployment. To address these issues, this paper proposes the BiT-Align image-depth-text affordance mapping framework. The framework includes a Bypass Prompt Module (BPM) and a Text Feature Guidance (TFG) attention selection mechanism. BPM integrates the auxiliary modality depth image directly as a prompt to the primary modality RGB image, embedding it into the primary modality encoder without introducing additional encoders. This reduces the model’s parameter count and effectively improves functional region localization accuracy. The TFG mechanism guides the selection and enhancement of attention heads in the image encoder using textual features, improving the understanding of affordance characteristics. Experimental results demonstrate that the proposed method achieves significant performance improvements on public AGD20K and HICO-IIF datasets. On the AGD20K dataset, compared with the current state-of-the-art method, we achieve a 6.0% improvement in the KLD metric, while reducing model parameters by 88.8%, demonstrating practical application values. The source code will be made publicly available at https://***/DAWDSE/BiT-Align. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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RAMPGrasp: Retentive Attention-based Multiscale perception Grasp Detection Network

引用

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Huang, Jianan Liu, Xuebing Zhu, Qing Wang, Yaonan Feng, Mingtao Zhou, Jiaming Zhou, Zhen Chen, Lin Wang, Danwei Hunan University The National Engineering Research Center for Robot Visual Perception and Control College of Electrical and Information Engineering Changsha410082 China Xidian University School of Artificial Intelligence Xi'an710071 China Nanyang Technological University School of Electrical and Electrical Engineering Nanyang Avenue 639798 Singapore

In robotic grasp detection, challenges such as uncertainty in object type, size, and placement within the scene diminish grasping accuracy. However, the inability to effectively locate the graspable area and incomplete feature extraction for grasp detection are two key factors that hinder grasp detection accuracy and are not considered in current methods. This paper presents a novel retentive attention-based multiscale perception grasp detection network (RAMPGrasp) to address this constraint. First, we introduce retentive attention in the feature extraction module, which significantly improves the efficiency of attention score computation for long sequences in visual tasks. Second, we propose a multiscale spatial pyramid attention module, which can effectively adjust the importance of multiscale feature sequences and feature channels, while enhancing the correlation of multiscale features. Third, we design the prediction module as a coarse-to-fine framework, improving feature representation for grasp detection by considering the distribution trend of grasp poses. As a result, RAMPGrasp achieves state-of-the-art grasp detection accuracy, with 98.4% and 95.6% on the Cornell and Jacquard datasets, respectively. © 1991-2012 IEEE.

关键词： robotics

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