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Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation patterns

Run-time Monitoring of 3D Object Detection in Automated Driv...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hakan Yekta Yatbaz Mehrdad Dianati Konstantinos Koufos Roger Woodman WMG University of Warwick School of Electronics Electrical Engineering and Computer Science (EEECS) Queen's University of Belfast

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring the integrity of object detection for errors within the perception module of automated driving systems (ADS) is paramount for ensuring safety. Despite recent advancements in deep neural network (DNN)-based object detectors, their susceptibility to detection errors, particularly in the less-explored realm of 3D object detection, remains a significant concern. State-of-the-art integrity monitoring (also known as introspection) mechanisms in 2D object detection mainly utilise the activation patterns in the final layer of the DNN-based detector’s backbone. However, that may not sufficiently address the complexities and sparsity of data in 3D object detection. To this end, we conduct, in this article, an extensive investigation into the effects of activation patterns extracted from various layers of the backbone network for introspecting the operation of 3D object detectors. Through a comparative analysis using Kitti and NuScenes datasets with PointPillars and CenterPoint detectors, we demonstrate that using earlier layers’ activation patterns enhances the error detection performance of the integrity monitoring system, yet increases computational complexity. To address the real-time operation requirements in ADS, we also introduce a novel introspection method that combines activation patterns from multiple layers of the detector’s backbone and report its performance.

关键词： computer vision Three-dimensional displays conferences Object detection Detectors Real-time systems Safety

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PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet Laws

PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet La...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Bor-Shiun Wang Ping-Yang Chen Yi-Kuan Hsieh Jun-Wei Hsieh Ming-Ching Chang JiaXin He Shin-You Teng HaoYuan Yue Yu-Chee Tseng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan College of AI and Green Energy National Yang Ming Chiao Tung University Tainan Taiwan Department of Computer Science University at Albany State University of New York NY USA Department of Computer Science Vanderbilt University USA Department of Computer Science The Chinese University of Hong Kong China

We present a video analytic system for enforcing motorcycle helmet regulation as a participation to the AI City Challenge 2023 [18] Track 5 contest. The advert of powerful object detectors enables real-time localization of the road users and even the ability to determine if a motorcyclist or a rider is wearing a helmet. Ensuring road safety is important, as the helmets can effectively provide protection against severe injuries and fatalities. However, monitoring and enforcing helmet compliance is challenging, given the large number of motorcyclists and limited visual input such as occlusions. To address these challenges, we propose a novel two-step approach. First, we introduce the PRB-FPN+, a state-of-the-art detector that excels in object localization. We also explore the benefits of deep supervision by incorporating auxiliary heads within the network, leading to enhanced performance of our deep learning architectures. Second, we utilize an advanced tracker named SMILEtrack to associate and refine the target track-lets. Comprehensive experimental results demonstrate that the PRB-FPN+ outperforms the state-of-the-art detectors on MS-COCO. Our system achieved a remarkable rank of 8 on the AI City Challenge 2023 [18] Track 5 Public Leader-board. Code implementation is available at: https://***/NYCU-AICVLab/AICITY_2023_Track5.

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 ieee.

关键词： Data acquisition

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Scaling Graph Convolutions for Mobile vision

Scaling Graph Convolutions for Mobile Vision

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： William Avery Mustafa Munir Radu Marculescu The University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

To compete with existing mobile architectures, Mobile-ViG introduces Sparse vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, Mobile-ViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 AP box and 0.7 AP mask , and MobileViGv2-B outperforms MobileViG-B by 1.0 AP box and 0.7 AP mask . For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% mIoU and MobileViGv2-B achieves 44.3% mIoU 1 .

关键词： Instance segmentation Accuracy Convolution Semantic segmentation Message passing computer architecture Object detection

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Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions

Feature Corrective Transfer Learning: End-to-End Solutions t...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Chuheng Wei Guoyuan Wu Matthew J. Barth University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in the field of object detection lies in the system’s performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces ‘Feature Corrective Transfer Learning’, a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model’s ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm.

关键词： Visualization computer vision Accuracy Computational modeling Surveillance Transfer learning Imaging

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Towards Weakly-Supervised Domain Adaptation for Lane Detection

Towards Weakly-Supervised Domain Adaptation for Lane Detecti...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jingxing Zhou Chongzhe Zhang Jürgen Beyerer Porsche Engineering Group GmbH Vision and Fusion Laboratory (IES) Karlsruhe Institute of Technology University of Stuttgart Fraunhofer IOSB

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Lane detection plays an indispensable role in automated driving functions and advanced driver assistance systems by providing fundamental spatial orientation, which is imperative for trajectory planning with traffic regulation compliance. The variability of lane structures across the world poses challenges for data-driven lane detection models. However, acquiring vast amounts of labeled data encompassing a wide variety of real-world scenarios for supervised learning is often cost-prohibitive. In this work, we propose a Weakly Supervised Domain Adaptation framework for Lane Detection (WSDAL), which requires easily-provided labels exclusively for the number of lanes in the target domain to aid the adaptation process. WSDAL consists of a teacher-student network, an additional segmentation head as an auxiliary task during training, and a novel loss function that incorporates the number of lanes prediction. As a versatile framework, WSDAL can be applied to any anchor-based lane detector. Between three frequently-used lane detection datasets (TuSimple, CULane and CurveLanes) for domain adaptation, WSDAL framework demonstrates its effectiveness and efficiency over common unsupervised domain adaptation methods and fully supervised training. In addition, we discuss the quality requisites from the labels for the weakly-supervised domain adaptation, indicating that label errors at realistic scales still provide satisfactory results on the considered tasks.

关键词： Training Image segmentation Adaptation models Lane detection Trajectory planning Supervised learning Detectors

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Photorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5

Photorealistic Arm Robot Simulation for 3D Plant Reconstruct...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xingjian Li Jeremy Park Chris Reberg-Horton Steven Mirsky Edgar Lobaton Lirong Xiang Department of Biological and Agricultural Engineering North Carolina State University Department of Electrical and Computer Engineering North Carolina State University Department of Computer Science North Carolina State University Department of Crop and Soil Science North Carolina State University Sustainable Agricultural Systems Laboratory USDA-ARS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Robotics paired with computer vision are widely used in precision agriculture. Simulations are critical for safety and performance estimation by verifying their routine in a virtual world before real-world testing and deployment. However, many simulators used in agricultural robots lack photorealism in their virtual worlds compared to the real world. We implemented Unreal Engine 5 (UE5) and the Robot Operating System (ROS) to develop a robot simulator tailored to agricultural tasks and synthetic data generation with RGB, segmentation, and depth images. We designed a method for assigning multiple segmentation labels within a single plant mesh. We experimented with a semi-spherical routine for two robot arms to perform 3D point cloud reconstruction across 10 plant assets. We showed our simulator produces much more accurate segmentation images and reconstruction compared to existing UE5 solutions. We extend our results with Neural Radiance Field (NeRF) reconstructions. The packaged simulator, UE5 project, and ROS package with the Python routine can be found at https://***/NCSU-BAE-ARLab/AgriRoboSimUE5.

关键词： Image segmentation computer vision Three-dimensional displays Computational modeling Neural radiance field Manipulators Image reconstruction

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NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Taehoon Kim Pyunghwan Ahn Sangyun Kim Sihaeng Lee Mark Marsden Alessandra Sala Seung Hwan Kim Bohyung Han Kyoung Mu Lee Honglak Lee Kyounghoon Bae Xiangyu Wu Yi Gao Hailiang Zhang Yang Yang Weili Guo Jianfeng Lu Youngtaek Oh Jae Won Cho Dong-Jin Kim In So Kweon Junmo Kim Wooyoung Kang Won Young Jhoo Byungseok Roh Jonghwan Mun Solgil Oh Kenan Emir Ak Gwang-Gook Lee Yan Xu Mingwei Shen Kyomin Hwang Wonsik Shin Kamin Lee Wonhark Park Dongkwan Lee Nojun Kwak Yujin Wang Yimu Wang Tiancheng Gu Xingchang Lv Mingmao Sun

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project 1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Training Adaptation models Visualization computer vision Computational modeling conferences Training data

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OGRMPI: An Efficient Multiview Integrated Multiplane Image based on Occlusion Guided Residuals

OGRMPI: An Efficient Multiview Integrated Multiplane Image b...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dae Yeol Lee Guan-Ming Su Peng Yin Dolby Laboratories Sunnyvale CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multiplane Image (MPI) is a volumetric scene representation method that uses multiple layers of texture (RGB) and alpha (A) planes. It offers high deployability due to its compatibility with standard codecs and low rendering complexity. While existing MPI methods have shown promising results, they are constrained by either a limited range of pose span or necessitates transmitting multiple MPIs. In this paper, we present a novel framework to generate an efficient single MPI that integrates the information from multiple views. The key idea is to utilize the surface opacity estimates (A) to locate and retrieve occluded RGB pixels from other camera views that share matching depth, which we call Occlusion Guided Residuals (OGR). Additionally, we introduce an inter-layer texture filler, which is a learned RGB texture on intermediate depth between MPI layers to deal with scenes with continuous depth with a limited number of MPI layers. We composite MPI using the aforementioned RGB textures and refine alpha layers through training with multiview rendering supervision. Thus, through iterations of training, we jointly optimize scene opacity (A) and textures (RGB) leading to an accurate MPI representation. Experiments on various multiview image and video datasets demonstrate that the proposed method achieves state-of-the-art performances with data efficiency. Notably, with just 16 layers, the proposed method attains performance on par with other methods that use twice the layer number or fuse multiple MPIs.

关键词： Training Interpolation computer vision Accuracy Three-dimensional displays Fuses conferences

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Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation

Diagnostic Benchmark and Iterative Inpainting for Layout-Gui...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jaemin Cho Linjie Li Zhengyuan Yang Zhe Gan Lijuan Wang Mohit Bansal UNC Chapel Hill Microsoft Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets with similar spatial configurations. However, it is unclear how these models perform when facing out-of-distribution (OOD) samples with arbitrary, unseen layouts. In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape. We benchmark two recent representative layout-guided image generation methods and observe that the good ID layout control may not generalize well to arbitrary layouts in the wild (e.g., objects at the boundary). Next, we propose IterInpaint, a new baseline that generates foreground and background regions step-by-step via inpainting, demonstrating stronger generalizability than existing models on OOD layouts in LayoutBench. We perform quantitative and qualitative evaluation and fine- grained analysis on the four LayoutBench skills to pinpoint the weaknesses of existing models. We show comprehensive ablation studies on IterInpaint, including training task ratio, crop&paste vs. repaint, and generation order. Lastly, we evaluate the zero-shot performance of different pretrained layout-guided image generation models on LayoutBench-COCO, our new benchmark for OOD layouts with real objects, where our IterInpaint consistently outperforms SOTA baselines in all four splits.

关键词： Training computer vision Image synthesis Shape conferences Layout Benchmark testing

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