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10th European Workshop on Visual Information Processing (EUVIP)

作者： Neri, Michael Battisti, Federica Roma Tre Univ Dept Ind Elect & Mech Engn Rome Italy Univ Padua Dept Informat Engn Padua Italy

ISBN: (数字)9781665466233

ISBN: (纸本)9781665466233

Accurate detection and classification of objects in 3d point clouds is a central problem in several applications such as autonomous navigation and augmented/virtual reality scenarios. In this paper we present a deep learning strategy for 3d object detection for railway applications based on the VoxelNet model. due to the lack of publicly available annotated data, we created a virtual railway environment for generating a synthetic annotated railway point cloud dataset. This approach allows to model shapes and locations of target landmarks such as traffic lights and railtracks. The achieved results show that our network learns an effective representation of railway landmarks using only raw LidAR point clouds, leading to encouraging results and possible future implementations in this research field.

关键词： deep Learning 3d object detection Point Clouds Railway dataset LidAR

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3d object detection based on Multi-View Feature Point Matching

3D Object Detection based on Multi-View Feature Point Matchi...

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Annual Conference of Chinese-Society-of-Optical-Engineering (CSOE) - AI in Optics and Photonics (AOPC)

作者： Yang, Tian Sang, Xinzhu Chen, duo Guo, Nan Wang, Peng Yu, Xunbo Yan, Binbin Wang, Kuiru Yu, Chongxiu Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

ISBN: (数字)9781510634572

ISBN: (纸本)9781510634572

The result of object detection based on deep learning may have errors or omissions due to the occlusion and background in object detection, which is an intractable problem. An effective method of improving object detection performance using multiple viewpoint images are proposed. By performing feature point matching on objects in the overlap between different views, groups of points with semantic information can be obtained. These point groups can be used to generate new detection boxes, which can correct error ones in the raw results. Experiments show that the proposed method is a viable solution, the recall is significantly improved.

关键词： 3d object detection multi-view semantic points feature point matching

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3d object detection on large-scale dataset

3D Object Detection on large-scale dataset

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhao, Yan Zhu, Jihong Liang, Haoyu Chen, Lyujie Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Dept Precis Instrument Beijing Peoples R China

ISBN: (纸本)9780738133669

3d object detection based on LidAR point cloud has gained more and more attention from industry and academia. Most of the previous works are carried out on the KITTI dataset, which has the gap with real-world scenes in terms of data size and distribution. Faced with large-scale datasets, existing methods have encountered with some problems, especially in the detection of difficult targets and heading prediction. In this paper, we design an integrated framework to improve 3d object detection accuracy targeting on large-scale datasets. We explore data augmentation methods from the perspective of data-rich scenes. Two well-designed auxiliary losses are introduced into our framework to relieve the problem caused by the diversity of points density and distribution. To promote the performance of heading prediction, the existing heading classifier is replaced by our novel dual heading classifier. Experiments on the largest and most realistic 3d object detection benchmark of Waymo Open dataset show that our integrated framework boosts the detection accuracy for a large margin.

关键词： 3d object detection large-scale datasets data augmentation auxiliary loss dual heading prediction

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3d object detection for Autonomous driving: A Survey 36

3D Object Detection for Autonomous Driving: A Survey

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36th Chinese Control and decision Conference (CCdC)

作者： Jin, JunXin Liu, Wei Ning, Zuotao Zhao, Qixi Cheng, Shuai Hu, Jun Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Automot Technol Ltd Neusoft Reach Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In recent years, autonomous driving has attracted significant attention. 3d object detection is a crucial component of autonomous driving systems, because it provides essential information for downstream tasks such as target tracking, path planning, and obstacle avoidance. So, it is meaningful to review existing 3d object detection methods and highlight their strengths and limitations. LidAR and camera sensors are commonly used by autonomous vehicles to collect point clouds and images. Based on the type of sensors used, we roughly categorize 3d object detection methods into three classes: LidAR-only method, Camera-only method and LidAR-Camera fusion method. In this review, we further categorize each class into several subtypes, and describe the core ideas of the methods in each subtype. Furthermore, this paper critically evaluates their strengths and limitations. Finally, by using public datasets, a comparative performance analysis of the methods is conducted.

关键词： autonomous driving 3d object detection computer vision deep learning

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Adaptive and azimuth-aware fusion network of multimodal local features for 3d object detection

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NEUROCOMPUTING 2020年 411卷 32-44页

作者： Tian, Yonglin Wang, Kunfeng Wang, Yuang Tian, Yulin Wang, Zilei Wang, Fei-Yue Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China North China Univ Technol Beijing 100144 Peoples R China

This paper focuses on the construction of strong local features and the effective fusion of image and LidAR data for 3d object detection. We adopt different modalities of LidAR data to generate rich features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud. Our network mainly consists of three subnetworks: ground plane estimation network, region proposal network and adaptive fusion network. The ground plane estimation network extracts features of point cloud and predicts the parameters of a plane which are used for generating abundant 3d anchors. The region proposal network generates features of image and bird's eye view maps to output region proposals. To integrate heterogeneous image and point cloud features, the adaptive fusion network explicitly adjusts the intensity of multiple local features and achieves the orientation consistency between image and LidAR data by introducing an azimuth-aware fusion module. Experiments are conducted on KITTI dataset and the results validate the advantages of our aggregation of multimodal local features and the adaptive fusion network. (C) 2020 Published by Elsevier B.V.

关键词： 3d object detection Point cloud Multimodal fusion Ground plane fitting

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A Smart IoT Enabled End-to-End 3d object detection System for Autonomous Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第11期24卷 13078-13087页

作者： Ahmed, Imran Jeon, Gwanggil Chehri, Abdellah Anglia Ruskin Univ Sch Comp & Informat Sci Cambridge CB1 1PT England Incheon Natl Univ Dept Embedded Syst Engn Incheon 22012 South Korea Royal Mil Coll Canada Dept Math & Comp Sci Kingston ON K7K 7B4 Canada

Integration of advanced signal processing, image processing, deep learning, edge computing, and the Internet of Things (IoT) into vehicles allows intelligent automated vehicles to navigate autonomously in different environments. It is crucial for reliable and safe driving that an autonomous vehicle can accurately, effectively, and efficiently recognize, perceive, and observe the surrounding environments. Autonomous vehicles comprise advanced sensor technologies such as RGB cameras and LidaR that produce an extensive data set in the form of RGB images and 3d measurement points, also recognized as a point cloud. It is necessary to understand and interpret collected data information efficiently and to identify other road users, such as pedestrians and vehicles. Thus, we introduced a smart IoT-enabled deep learning based end-to-end 3d object detection system that works in real-time, emphasizing autonomous driving situations. The detection model is based on YOLOv3;firstly, the model is utilized for 2d object detection and then modified for 3d object detection purposes. The presented model uses point cloud, and RGB image data as input and outputs detected bounding boxes with confidence scores and class labels. Experiments are carried out on the Lyft data set;results reveal that the YOLOv3 model achieves high accuracy and outperforms from other state-of-the-art detection models in terms of effectiveness and accuracy. The overall accuracy of the model is 96% and 97% for 2d and 3d object detection, respectively.

关键词： Autonomous vehicles Internet of Things digital twins deep learning 3d object detection YOLOv3 lyft

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FS-3dSSN: an efficient few-shot learning for single-stage 3d object detection on point clouds

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VISUAL COMPUTER 2024年第11期40卷 8125-8139页

作者： Tiwari, Alok Kumar Sharma, G. K. ABV Indian Inst Informat Technol & Management Dept Informat Technol Gwalior India

The current 3d object detectionmethods have achieved promising results for conventional tasks to detect frequently occurring objects like cars, pedestrians and cyclists. However, they require many annotated boundary boxes and class labels for training, which is very expensive and hard to obtain. Nevertheless, detecting infrequent occurring objects, such as police vehicles, is also essential for autonomous driving to be successful. Therefore, we explore the potential of few-shot learning to handle this challenge of detecting infrequent categories. The current 3d object detectors do not have the necessary architecture to support this type of learning. Thus, this paper presents a new method termed few-shot single-stage network for 3d object detection (FS-3dSSN) to predict infrequent categories of objects. FS-3dSSN uses a class-incremental few-shot learning approach to detect infrequent categories without compromising the detection accuracy of frequent categories. It consists of twomodules: (i) a single-stage network architecture for 3d object detection (3dSSN) using deformable convolutions to detect small objects and (ii) a class-incremental-based meta-learning module to learn and predict infrequent class categories. 3dSSN obtained 84.53 mAP(3d) on the KITTI car category and 73.4 NdS on the nuScenes dataset, outperforming previous state of the art. Further, the result of FS-3dSSN on nuScenes is also encouraging for detecting infrequent categories while maintaining accuracy in frequent classes.

关键词： Point cloud 3d object detection Few-shot learning Single-stage detector Autonomous driving

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Adversarial point cloud perturbations against 3d object detection in autonomous driving systems

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NEUROCOMPUTING 2021年 466卷 27-36页

作者： Wang, Xupeng Cai, Mumuxin Sohel, Ferdous Sang, Nan Chang, Zhengwei Univ Elect Sci & Technol China Sch Informat & Software Engn 4Sect 2North Jianshe Rd Chengdu 610054 Peoples R China Murdoch Univ Informat Technol 90 South St Murdoch WA 6150 Australia State Grid Sichuan Elect Power Co 16 West Jinhui Rd Chengdu 610041 Peoples R China

deep learning models have been demonstrated vulnerable to adversarial attacks even with imperceptible perturbations. As such, the reliability of existing deep neural networks-based autonomous driving systems can suffer. However, deep 3d models have applications in various Cyber-Physical Systems (CPSs) with safety-critical requirements, particularly autonomous driving systems. In this paper, the robustness of deep 3d object detection models under adversarial point cloud perturbations has been investigated. A novel method is developed to generate 3d adversarial examples from point cloud perturbations, which are common due to the intrinsic characteristics of the data captured by 3d sensors, e.g., LidAR. The generation of adversarial samples is supervised by a dual loss, which constitutes an adversarial loss and a perturbation loss. The adversarial loss produces a point cloud with the property of aggressiveness, while the perturbation loss enforces the produced point cloud subject to visual imperception. We demonstrate that the method can successfully attack 3d object detection models in most cases, and expose their vulnerability to physical-world attacks in the form of point cloud perturbations. We perform a thorough evaluation of popular deep 3d object detectors in an adversarial setting on the KITTI vision benchmark. Experimental results show that current deep 3d object detection models are susceptible to adversarial attacks in the context of autonomous driving, and their performances are degraded by a large margin in the presence of adversarial point clouds generated by the proposed method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Adversarial attack Robustness of deep learning 3d object detection Point cloud perturbations Autonomous driving systems

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Exploring diversity-Based Active Learning for 3d object detection in Autonomous driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15454-15466页

作者： Lin, Jinpeng Liang, Zhihao deng, Shengheng Cai, Lile Jiang, Tao Li, Tianrui Jia, Kui Xu, Xun Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 610032 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Weride Guangzhou 510700 Huangpu Peoples R China ASTAR Inst Infocomm Res I2R Singapore 138632 Singapore Chengdu Univ Tradit Chinese Med Sch Intelligent Med Chengdu 610075 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen Peoples R China

3d object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3d bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurements, where the realistic costs for annotating a frame and a 3d bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.

关键词： Three-dimensional displays object detection Costs Annotations detectors Uncertainty diversity reception Feature extraction Autonomous vehicles Point cloud compression Active learning 3d object detection autonomous driving

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3d object detection, Instance Segmentation and Classification from 3d Range and 2d Color Images

3D Object Detection, Instance Segmentation and Classificatio...

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作者： Shen, Xiaoke City University of New York

学位级别：Ph.D.

We address the problem of 3d object detection and instance segmentation by proposing a novel object segmentation and detection system. First, we detect 2d objects based on RGB, depth only, or RGB-d images. A 3d convolutional-based system, named Frustum VoxNet, is proposed. This system 1) generates frustums from 2d detection results, 2) proposes 3d candidate voxelized images for each frustum, and uses a 3d convolutional neural network (CNN) based on these candidates voxelized images to perform the 3d instance segmentation and object detection. Although the volumetric data representation is widely used for 3d object classiﬁcation, there are fewer works on 3d object detection based on this representation. Volumetric representations are advantageous compared with raw point clouds. First, they naturally support convolution and deconvolution operations, which play essential roles in object classification and segmentation tasks. Second, the memory requirements of this representation will not be increased when denser cloud points are collected. Hence, the computational complexity of the system will not be influenced. Third, stable inference results can be guaranteed as the sub-sampling of input data is unnecessary. This is in contrast with the fact that methods relying on non-voxelized point clouds have to sub-sample the input data due to complexity limitations. Results on the SUNRGB-d dataset show that our RGB-d based system can achieve better detection results based on several categories. Our inference speed is much faster than state of the art. At the same time, our depth only system can achieve results that are comparable to RGB-d based systems. Our improved system can achieve excellent 3d instance segmentation results. Also, the 3d object detection based on the instance segmentation results can further improve the detection performance.

关键词： 3d object detection Instance Segmentation Frustum VoxNet 3d CNN

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