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Multi-task learning andjoint refinement between camera localization and object detection

Computational Visual Media 2024年第5期10卷 993-1011页

作者： Junyi Wang Yue Qi State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518052China Qingdao Research Institute of Beihang University Qingdao 266104China School of Computer Science and Technology Shandong UniversityQingdaoChina

Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such *** this paper,we explore multi-task network design and joint refinement of detection and *** address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic *** dataset provides localization and detection information,and is publicly available at https://***/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection *** this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding *** dynamic environments,the detection branch also promotes the perception of *** includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud ***,object-level bundle adjustment is used to further improve localization and detection *** test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn *** results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.

关键词： visual localization object detection joint optimization multi-task learning

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Moving object detection in aerial video based on spatiotemporal saliency

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Chinese Journal of Aeronautics 2013年第5期26卷 1211-1217页

作者： Shen Hao Li Shuxiao Zhu Chengfei Chang Hongxing Zhang Jinglan Institute of Automaton Chinese Academy of Sciences Queensland University of Technology BrisbaneAustralia

In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object＇s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.

关键词： Aerial video Computer vision object detection Saliency Unmanned aerial vehicles

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A comprehensive analysis of DAC-SDC FPGA low power object detection challenge

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Science China(Information Sciences) 2024年第8期67卷 300-320页

作者： Jingwei ZHANG Guoqing LI Meng ZHANG Xinye CAO Yu ZHANG Xiang LI Ziyang CHEN Jun YANG School of Electronics Science and Engineering Southeast University

The lower power object detection challenge(LPODC) at the IEEE/ACM Design Automation Conference is a premier contest in low-power object detection and algorithm(software)-hardware co-design for edge artificial intelligence, which has been a success in the past five years. LPODC focused on designing and implementing novel algorithms on the edge platform for object detection in images taken from unmanned aerial vehicles(UAVs), which attracted hundreds of teams from dozens of countries to participate. Our team SEUer has been participating in this competition for three consecutive years from 2020 to 2022 and obtained sixth place respectively in 2020 and 2021. Recently, we achieved the championship in 2022. In this paper, we presented the LPODC for UAV object detection from 2018 to 2022, including the dataset, hardware platform,and evaluation method. In addition, we also introduced and discussed the details of methods proposed by each year's top three teams from 2018 to 2022 in terms of network, accuracy, quantization method, hardware performance, and total score. Additionally, we conducted an in-depth analysis of the selected entries and results, along with summarizing representative methodologies. This analysis serves as a valuable practical resource for researchers and engineers in deploying the UAV application on edge platforms and enhancing its feasibility and reliability. According to the analysis and discussion, it becomes evident that the adoption of a hardware-algorithm co-design approach is paramount in the context of tiny machine learning(TinyML).This approach surpasses the mere optimization of software and hardware as separate entities, proving to be essential for achieving optimal performance and efficiency in TinyML applications.

关键词： tiny machine learning object detection convolutional neural networks algorithm-hardware co-design low power field programmable gate array

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GridNet-3D: A Novel Real-Time 3D object detection Algorithm Based on Point Cloud

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Chinese Journal of Electronics 2021年第5期30卷 931-939页

作者： YUE Yuanchen CAI Yunfei WANG Dongsheng School of Computer Science and Engineering Nanjing University of Science and Technology

3D object detection based on point cloud has an important application prospect in automatic driving technology. Aiming at the low precision of 3D object detection based on point cloud and the poor real-time performance caused by large numbers of 3D convolutions, a novel end-to-end real-time object detection algorithm named GridNet-3D is proposed. In the work, 2D gridmapping is used to preprocess the original point *** a novel structure grid encoding layer is adopted to encode point cloud features and is gotten grid feature maps in bird's eye view which is connected to region proposal network module to generate detections. Despite only using point clouds, the results on the KITTI 3D detection benchmark show that our algorithm has higher detection precision and better real-time performance on the detection of cars, pedestrians and cyclists, which has high practical value.

关键词： GridNet-3D point cloud features region proposal network module structure grid encoding layer grid feature maps 2D gridmapping intelligent transportation systems end-to-end real-time object detection algorithm KITTI 3D detection benchmark feature extraction automatic driving technology object detection 3D convolution

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Visual SLAM in dynamic environments based on object detection

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Defence Technology（防务技术） 2021年第5期17卷 1712-1721页

作者： Yong-bao Ai Ting Rui Xiao-qiang Yang Jia-lin He Lei Fu Jian-bin Li Ming Lu College of Field Engineering People’s Liberation Army Engineering UniversityNanjing210007China JinKen College of Technology Nanjing211156China Research Institute of Chemical Defense Academy of Military SciencesBeijing102205China

A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the ***,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world *** cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was *** reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our *** with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.

关键词： Visual SLAM object detection Dynamic object probability model Dynamic environments

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Dual Attribute Adversarial Camouflage toward camouflaged object detection

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Defence Technology（防务技术） 2023年第4期22卷 166-175页

作者： Yang Wang Zheng Fang Yun-fei Zheng Zhen Yang Wen Tong Tie-yong Cao Institute of Command and Control Engineering Peoples Liberation Army Engineering UniversityNanjing210007China The Army Artillery and Defense Academy of PLA Nanjing210007China PLA University of Foreign Language Luoyang471000China

The object detectors can precisely detect the camouflaged object beyond human *** investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial *** works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong *** is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous *** by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and *** DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of *** visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene *** results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital *** work can serve as the reference for crafting the adversarial camouflage in the physical world.

关键词： Adversarial camouflage Digital camouflage generation Visual concealment object detection Adversarial patch

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Enhancing the robustness of object detection via 6G vehicular edge computing

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Digital Communications and Networks 2022年第6期8卷 923-931页

作者： Chen Chen Guorun Yao Chenyu Wang Sotirios Goudos Shaohua Wan School of Telecommunication Engineering Xidian UniversityXi'an710071China Department of Physics ELEDIA@AUTHAristotle University of Thessaloniki54124ThessalonikiGreece Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen518110China

Academic and industrial communities have been paying significant attention to the 6th Generation (6G) wireless communication systems after the commercial deployment of 5G cellular communications. Among the emerging technologies, Vehicular Edge Computing (VEC) can provide essential assurance for the robustness of Artificial Intelligence (AI) algorithms to be used in the 6G systems. Therefore, in this paper, a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed, taking the object detection task as an example. This strategy includes two stages: model stabilization and model adaptation. In the former, the state-of-the-art methods are appended to the model to improve its robustness. In the latter, two targeted compression methods are implemented, namely model parameter pruning and knowledge distillation, which result in a trade-off between model performance and runtime resources. Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals, where the introduced trade-off outperforms the other strategies available.

关键词： 6G Vehicular edge computing object detection Feature fusion Model compression Model deployment

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Tiny YOLO Optimization Oriented Bus Passenger object detection

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Chinese Journal of Electronics 2020年第1期29卷 132-138页

作者： ZHANG Shuo WU Yanxia MEN Chaoguang LI Xiaosong College of Computer Science and Technology Harbin Engineering University

The real-time collection of bus passenger object detection is an essential part of developing a smart bus system. The difficulty of object detection mainly lies in the objective factors, such as: clothing, hair style and accessories, light, etc. Traditional object detection methods with the artificial feature extraction suffers from insufficient strength in expression, generalization, and recognition rate. The object detection method based on deep learning mainly uses the convolutional neural network in deep learning to learn features from a large set of *** learned features can describe the rich information inherent in the data, and improve the expression ability of the features as well as the recognition accuracy. Due to too many parameters of the Convolutional neural network(CNN) model, the amount of calculation is too large to be operated on the vehicle terminal. To reduce calculation burden and improve the operation speed, we employs the depthwise separable convolution method to optimize the convolutional layer of tiny YOLO network model. It decomposes a complete convolution operation into depthwise convolution and pointwise convolution,thus reducing the parameter amount of the CNN and improving the operation speed. The experiment results reveal that the speed of bus passenger object detection detected by our improved model is 4 times faster than the previous one but with the nearly same detection accuracy.

关键词： Bus system object detection Model optimization Tiny YOLO

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Long-tailed object detection of kitchen waste with class-instance balanced detector

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Science China(Technological Sciences) 2023年第8期66卷 2361-2372页

作者： FANG LeYuan TANG Qi OUYANG LiHan YU JunWu LIN JiaXing DING ShuaiYu TANG Lin College of Electrical and Information Engineering Hunan UniversityChangsha 410082China College of Environmental Science and Engineering Hunan UniversityChangsha 410082China

Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual ***,the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution,making it challenging to train convolutional neural network-based object detectors,which results in the unsatisfactory detection of tailclass *** address this challenge,we propose a class-instance balanced detector(CIB-Det) for intelligent detection and classification of kitchen ***-Det implements two strategies for the loss function:the class-balanced strategy(CBS)and the instance-balanced strategy(IBS).The CBS focuses more on tail classes,and the IBS concentrates on hard-to-classify instances adaptively during ***,CIB-Det comprehensively and adaptively addresses the long-tailed *** experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.

关键词： kitchen waste detection and classification object detection long-tailed distribution convolutional neural networks

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Low-altitude small-sized object detection using lightweight feature-enhanced convolutional neural network

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Journal of Systems Engineering and Electronics 2021年第4期32卷 841-853页

作者： YE Tao ZHAO Zongyang ZHANG Jun CHAI Xinghua ZHOU Fuqiang School of Mechanical and Electrical Information Engineering China University of Mining and Technology-BeijingBeijing 100083China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050081China School of Instrumentation Science and Opto-electronics Engineering Beihang UniversityBeijing 100083China

Unauthorized operations referred to as“black flights”of unmanned aerial vehicles(UAVs)pose a significant danger to public safety,and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and ***,their accuracy is insufficient,particularly for small objects in complex *** solve these problems,we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying *** proposed network consists of three modules.A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature,an enhanced feature processing module significantly improves the feature extraction ability of the model,and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments,particularly for small *** proposed method achieves a detection speed of 147 frames per second(FPS)and a mean average precision(mAP)of 90.97%for a dataset composed of flying objects,indicating its potential for low-altitude object ***,evaluation results based on microsoft common objects in context(MS COCO)indicate that the proposed method is also applicable to object detection in general.

关键词： unmanned aerial vehicle(UAV) deep learning lightweight network object detection low-attitude

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