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Research on UAV Detection Technology of Gm-APD Lidar Based on YOLO Model

Research on UAV Detection Technology of Gm-APD Lidar Based o...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： DIng, Yuanxue Qu, Yanchen Zhang, Qing Tong, Jiahui Yang, Xianhui Sun, Jianfeng Harbin Institute of Technology National Key Laboratory of Science and Technology on Tunable Laser Institution of Opto-Electronics Harbin China Beijing Electromechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9780738146577

In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly, which plays an important role in both military and civil fields. While it brings convenience to all walks of life, there are also a lot of security problems. Therefore, it is necessary to study anti-UAV technology, but the detection of UAV is an important basis of anti-UAV technology. In this paper, by combining the three-dimensional range profile collected by Gm-APD (Geiger mode Avalanche Photo Diode) lidar with the deep learning method, a UAV detection method based on the improved YOLOv3 (You Only Look Once v3) network is proposed. Firstly, the data of UAV in low altitude real scene is collected by Gm-APD lidar, and range profile information is generated. Then, the YOLOv3 network is improved, and the SPP module can be added to combine the local features of the target with the global features to improve the detection accuracy. It is suitable for the detection of UAVs. Finally, the trajectory of the UAV is drawn based on the distance information. The experimental results show that the improved method can effectively detect UAV targets, and is better than the previous method, YOLOv3-Tiny method and YOLOv4 method. The farthest detection distance is 238m, the detection speed is fast, and the purpose of real-time detection and positioning can be achieved. Therefore, this method can be applied to anti-UAV technology and has important research significance. © 2021 IEEE.

关键词： Optical radar

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Qi, Xianzhi Fu, Wenxing An, Pei Wu, Bingli Ma, Jie National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100071 China

ISBN: (纸本)9781728152240

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy. © 2020 IEEE.

关键词： Wakes

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Experimental research on GM-APD LIDAR point cloud classification algorithm based on deep learning

Experimental research on GM-APD LIDAR point cloud classifica...

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2022 Applied Optics and Photonics China: Advanced Laser Technology and Applications, AOPC 2022

作者： Jiang, Yanze Sun, Jianfeng Ding, Yuanxue Jiang, Peng Zhang, Hailong Li, Sining Zhou, Shuaijun National Key Laboratory of Science and Technology on Tunable Laser Institute of OptoElectronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9781510662223

Geiger mode Avalanche Photo Diode (Gm-APD) array lidar is a lidar that can perform single-photon detection. It offers a wide range of applications due to its low power consumption, small size, and extended detecting distance. There haven't been many research on this detector's target classification because of its late development and small detector array. The classification technique based on the Gm-APD array lidar point cloud is the focus of this paper's research: Firstly, the Gm-APD array lidar is utilized to perform imaging tests on four targets from various angles in order to create a target classification *** that, several data preprocessing methods were chosen and implemented based on the characteristics of the obtained data, such as filling in missing values, performing range image and intensity image interpolation, using the principle of keyhole imaging to convert the range image to point cloud data, realizing the information fusion of distance image and intensity image, and using multiple point cloud data enhancement methods. Finally, the point cloud classification networks PointNet and PointNet++ are trained on point cloud data with varying levels of preprocessing, the results are compared and analyzed, and the impact of different preprocessing methods on the classification accuracy of the two networks is determined. Inferences were made and experiments were carried out to verify the inferences. The data set preprocessing method with the highest classification accuracy of the two networks is discovered, laying the groundwork for future Gm-APD lidar target classification and detection research. © 2023 SPIE.

关键词： Optical radar

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Estimation of smoke echo signal using single-photon lidar 13

Estimation of smoke echo signal using single-photon lidar

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13th International Conference on Information Optics and Photonics, CIOP 2022

作者： Zhang, Yinbo Li, Sining Sun, Jianfeng Jiang, Peng Zhou, Shuaijun Zhang, Xin Hou, Yue Zhang, Hailong National Key Laboratory of Science and Technology on Tunable Laser Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9781510660632

The non-uniform distribution of smoke and laser spot seriously limits the imaging ability of single-photon lidar through smoke. To this end, based on the collision theory between photons and smoke particles, this paper establishes the time-domain distribution model of scattered photons (Gamma), which considers the lidar system parameters and the characteristics of smoke particles. The shape parameter and inverse scale parameter are defined as the maximum scattering number K and the average scattering number β, respectively. The indoor test effectively illustrates the estimation ability of the model for smoke. The parameter estimation results show that the average scattering number β and the maximum scattering number K increase linearly with the increase of attenuation coefficient. This study is of great significance for the suppression of smoke backscattering and is expected to improve the weather adaptability of single-photon lidar. © 2022 SPIE.

关键词： Backscattering

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The Current Research Status of Unmanned Aerial Vehicle Maneuver Penetration Decision-Making

The Current Research Status of Unmanned Aerial Vehicle Maneu...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yutao Zhuang National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing P. R. China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

In modern warfare, maneuver penetration is an essential aspect of unmanned aerial vehicle (UAV) combat operations. As the capabilities of both offensive and defensive sides continue to improve, the operational patterns of maneuver penetration scenarios are constantly evolving. This paper first introduces the maneuver capabilities and detection characteristics of both offensive and defensive systems in contemporary warfare, providing a macro definition of the maneuver penetration problem. Then, it conducts a survey and analysis of the applications of traditional and new artificial intelligence algorithms in maneuver penetration decision-making. Finally, it discusses the future research directions and challenges of artificial intelligence algorithms in this field and envisions the development of maneuver penetration decision-making.

关键词： Surveys Heuristic algorithms Decision making Line-of-sight propagation Autonomous aerial vehicles Deep reinforcement learning Safety Trajectory Artificial intelligence Vehicle dynamics

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A Noise Filter for Dynamic Vision Sensor Based on Spatiotemporal Correlation and Hot Pixel Detection

A Noise Filter for Dynamic Vision Sensor Based on Spatiotemp...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Xu, Ninghui Zhao, Jiajia Ren, Yuan Wang, Lihui Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education School of Instrument Science and Engineering Southeast University Nanjing210096 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789811694912

As a new type of neuromorphic sensor, the events-driven Dynamic Vision Sensor (DVS) has the advantages of low latency and wide dynamic range. The development of DVS overcomes the application limitations of traditional cameras in the field of high speed robots and autonomous driving. However, there are abundant unwanted noise events contained in the output of DVS, which severely affects the subsequent data-processing. To tackle the problem, a novel noise filter for DVS is proposed in this paper. Firstly, since the noise is composed of hot pixel events with high-frequency and random background activities, a time window is constructed to continuously detect hot pixels. Secondly, a spatiotemporal correlation-based denoising approach for each incoming event is conducted, while excluding its surrounding hot pixels, to avoid the negative effect caused by their high temporal correlation on the filter. Finally, the hot pixels are compensated with the events that are most probably to occur depending on the adjacent events, to achieve the completeness and precision of the output event stream. Experiments in different scenes demonstrate that the proposed noise filter can effectively eliminate the noise from the event stream of DVS and obviously outperforms the baseline method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pixels

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Visual Explanation of Object Detectors via Saliency Maps 18

Visual Explanation of Object Detectors via Saliency Maps

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Xiao, Jin Liu, Wenrui Hu, Xiaoguang Jiang, Hao Wang, Weipeng Yahang, Li Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Beijing China The Third Academy of Casic Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350312201

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators. © 2023 IEEE.

关键词： Object detection

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A Multi-Robot Task Allocation Method Based on Graph Attention Network and Unsupervised Learning

A Multi-Robot Task Allocation Method Based on Graph Attentio...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Wu, Zirui Li, Zhen Zhu, Dong Liao, Qiuhan Yao, Weiran School of Astronautics Harbin Institute of Technology Harbin China Beijing Electro-Mechanical Engineering Institute Beijing China Chengdu CHG Robots & Intelligent Equipment Institute Co. Ltd. Chengdu China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (纸本)9798350384185

The task allocation for multiple robots is a critical component in the coordination of unmanned *** existing heuristic algorithms are hard to achieve satisfactory results in large-scale problems, and reinforcement learning-based methods face challenges in ensuring the rationality of reward *** paper introduces a model based on multi-head attention and graph neural networks to address the schedule-dependent multi-robot task allocation problem, trained using unsupervised learning *** model can be trained with varying numbers of robots and tasks without necessitating changes to its structure or *** the experiment of this paper, the model is trained under two different conditions, and the performance is evaluated across six different problem *** the proposed model against greedy algorithms and genetic algorithms, the results demonstrate that the proposed model has significant advantages in overall performance. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hierarchical Graph Search Method for Path Planning of Unmanned Ground Vehicle for Freight Transportation

A Hierarchical Graph Search Method for Path Planning of Unma...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Hu, Jianing Li, Zhen Guo, Sen Yao, Weiran School of Astronautics Harbin Institute of Technology Harbin China Beijing Electro-Mechanical Engineering Institute Beijing China CRRC Nanjing Puzhen Co. Ltd. Nanjing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (纸本)9798350384185

For the problem of collision volume change when forklift unmanned vehicles work in complex storage environments, a multi-level graph search path planning method is proposed. The first level is the path planning of the Unmanned Ground Vehicle (UGV) body, which uses the grid map and the A* algorithm to plan the path of the UGV transferring in the scene;the second level is the joint path planning of the UGV and the object being carried, to avoid overcomplicating this issue, the unmanned vehicle will be limited to pushing the cargo only forwards. The third level is the path planning when the unmanned vehicle carries multiple objects, which decomposes the whole scene into multiple sub-scenes, plans the paths of the unmanned vehicle in the sub-scenes separately, and searches for the optimal combination of sub-scenes. Higher level searches will invoke lower level algorithms and provide a virtual scene and target as input. The experimental results show that this multilevel path planning method is optimal and complete in solving the forklift unmanned vehicle for handling a single object, and can give a feasible solution for handling multiple objects. © 2024 IEEE.

关键词： Ground vehicles

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Vehicle detection based on point cloud intensity and distance clustering 5

Vehicle detection based on point cloud intensity and distanc...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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