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AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques

AADS: An Automated Accident Detection and Nighttime Surveill...

引用

2025 International conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Karthikeyan, M. Kshirsagar, Pravin Ramdas Tak, Tan Kuan Lalit, Keshav Upreti, Kamal Jain, Rituraj School of Computing Srm Institute of Science and Technology Dept. of Computing Technologies Chennai India J.D College of Engg & Mgmt. Department of Electronics Engg. MS Nagpur India Singapore Institute of Technology Singapore Manipal Institute of Technology Dept. of Computer Science & Engineering Karnataka Manipal India Christ University Department of Computer Science Delhi NCR Ghaziabad India Marwadi University Department of Info. Tech. Gujarat Rajkot India

ISBN: (纸本)9798331527495

computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection. © 2025 ieee.

关键词： Machine vision

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Information Theoretic Pruning of Coupled Channels in Deep Neural Networks

Information Theoretic Pruning of Coupled Channels in Deep Ne...

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ieee Workshop on Applications of computer vision (WACV)

作者： Peyman Rostami Nilotpal Sinha Nidhaleddine Chenni Anis Kacem Abd El Rahman Shabayek Carl Shneider Djamila Aouada SnT University of Luxembourg

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Variational channel pruning approaches have obtained impressive results thanks to their stochastic nature, well established foundation in information theory, and the practically appealing structured sparsity pattern they offer. Despite their success in pruning Plain Networks (PlainNets), their application has faced certain limitations in networks with structurally coupled channels such as ResNets. In such scenarios, not only is it required to prune structurally coupled channels together, but it is also necessary to ensure that the whole coupled group is irrelevant before pruning is applied. This is an under-investigated problem as most existing methods are designed without taking these couplings into account. In this paper, we propose a novel approach based on Information Theoretic Pruning of structurally Coupled Channels (ITPCC) in neural networks. IT-PCC allows for learning the probabilistic distribution of coupled channel set importance and prunes the ones with the least relevant information to the task at hand. Experimental results for image classification on CIFAR10, CI-FAR100, and ImageNet datasets show that the proposed method outperforms the state-of-the-art, more significantly at high compression rates.

关键词： Hands Couplings computer vision Image coding Neural network compression Artificial neural networks Probabilistic logic Information theory Image classification

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Prior2Posterior: Model Prior Correction for Long-Tailed Learning

Prior2Posterior: Model Prior Correction for Long-Tailed Lear...

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ieee Workshop on Applications of computer vision (WACV)

作者： S Divakar Bhat Amit More Mudit Soni Surbhi Agrawal Honda R&D Co. Ltd. Tokyo Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Learning-based solutions for long-tailed recognition face difficulties in generalizing on balanced test datasets. Due to imbalanced data prior, the learned a posteriori distribution is biased toward the most frequent (head) classes, leading to an inferior performance on the least frequent (tail) classes. In general, the performance can be improved by removing such a bias by eliminating the effect of imbalanced prior modeled using the number of class samples (frequencies). We first observe that the effective prior on the classes, learned by the model at the end of the training, can differ from the empirical prior obtained using class frequencies. Thus, we propose a novel approach to accurately model the effective prior of a trained model using a posteriori probabilities. We propose to correct the imbalanced prior by adjusting the predicted a posteriori probabilities (Prior2Posterior: P2P) using the calculated prior in a post-hoc manner after the training, and show that it can result in improved model performance. We present theoretical analysis showing the optimality of our approach for models trained with naive cross-entropy loss as well as logit adjusted loss. Our experiments show that the proposed approach achieves new state-of-the-art (SOTA) on several benchmark datasets from the long-tail literature in the category of logit adjustment methods. Further, the proposed approach can be used to inspect any existing method to capture the effective prior and remove any residual bias to improve its performance, post-hoc, without model retraining. We also show that by using the proposed post-hoc approach, the performance of many existing methods can be improved further.

关键词： Training computer vision Analytical models Heavily-tailed distribution Face recognition Tail Probability Predictive models Benchmark testing

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VG-SSL: Benchmarking Self-Supervised Representation Learning Approaches for Visual Geo-Localization

VG-SSL: Benchmarking Self-Supervised Representation Learning...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Jiuhong Xiao Gao Zhu Giuseppe Loianno New York University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Visual Geo-localization (VG) is a critical research area for identifying geo-locations from visual inputs, particularly in autonomous navigation for robotics and vehicles. Current VG methods often learn feature extractors from geo-labeled images to create dense, geographically relevant representations. Recent advances in Self-Supervised Learning (SSL) have demonstrated its capability to achieve performance on par with supervised techniques with unlabeled images. This study presents a novel VG-SSL framework, designed for versatile integration and benchmarking of diverse SSL methods for representation learning in VG, featuring a unique geo-related pair strategy, GeoPair. Through extensive performance analysis, we adapt SSL techniques to improve VG on datasets from hand-held and car-mounted cameras used in robotics and autonomous vehicles. Our results show that contrastive learning and information maximization methods yield superior geo-specific representation quality, matching or surpassing the performance of state-of-the-art VG techniques. To our knowledge, This is the first benchmarking study of SSL in VG, highlighting its potential in enhancing geo-specific visual representations for robotics and autonomous vehicles. The code is publicly available at https://***/arplaboratory/VG-SSL.

关键词： Representation learning Visualization Robot vision systems Image retrieval Contrastive learning Benchmark testing Feature extraction Performance analysis Autonomous vehicles Visual place recognition

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GroundingMate: Aiding Object Grounding for Goal-Oriented vision-and-Language Navigation

GroundingMate: Aiding Object Grounding for Goal-Oriented Vis...

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ieee Workshop on Applications of computer vision (WACV)

作者： Qianyi Liu Siqi Zhang Yanyuan Qiao Junyou Zhu Xiang Li Longteng Guo Qunbo Wang Xingjian He Qi Wu Jing Liu Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Department of Computer Science and Technology Tongji University Australian Institute for Machine Learning The University of Adelaide

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Goal-Oriented vision-and-Language Navigation (VLN) aims to enable agents to navigate to specified locations and identify designated target objects following natural language instruction. This approach has gained popularity due to its close alignment with real-world scenarios. However, existing studies have predominantly focused on enhancing navigation performance, neglecting the ability to locate objects at the navigation endpoint. This oversight has resulted in a significant discrepancy between the success rates of navigation and object grounding. The challenge is compounded by the complex reasoning required by the instructions and the necessity to synthesize multiperspective images of objects, which overwhelms traditional object grounding methods. We leverage the Multi-Modal Large Language Model (MLLM) to bridge this gap, allowing agents to seek assistance from these models when struggling to locate the target object. The agent conducts a multi-stage evaluation to discern the cause of its confusion and promptly extracts and updates the most relevant information for MLLM to assess. Our method is plug-and-play and model-agnostic, facilitating integration with numerous existing VLN strategies without the need for retraining. Implementing our approach across four distinct methods has improved performance on the REVERIE and SOON datasets, demonstrating the effectiveness and generalizability of our technique.

关键词： Bridges computer vision Navigation Grounding Large language models Computational modeling Natural languages Cognition Data mining Object recognition

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DiL: An Explainable and Practical Metric for Abnormal Uncertainty in Object Detection

DiL: An Explainable and Practical Metric for Abnormal Uncert...

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ieee Workshop on Applications of computer vision (WACV)

作者： Amit Giloni Omer Hofman Ikuya Morikawa Toshiya Shimizu Yuval Elovici Asaf Shabtai Ben-Gurion University of the Negev Fujitsu Research of Europe Fujitsu Limited

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Although object detection models are widely used, their predictive performance has been shown to deteriorate when faced with abnormal scenes. Such abnormalities can occur naturally (by partially occluded or out-of-distribution objects) or deliberately (in the case of an adversarial attack). Existing uncertainty quantification methods, such as object detection evaluation metrics and label-uncertainty quantification techniques, do not consider the abnormalities' effect on the model's internal decision-making process. Furthermore, practical methods that consider the effects of abnormalities (such as abnormality detection and mitigation) are designed to deal with one type of abnormality. We present distinctive localization (DiL), an unsupervised, practical and explainable metric that quantitatively interprets any type of abnormality and can be leveraged for preventive purposes. By utilizing XAI techniques (saliency maps), DiL maps the objectness of a given scene and captures the model's inner uncertainty regarding the identified (and missed) objects. DiL was evaluated across nine use cases, including partially occluded and out-of-distribution objects, as well as adversarial patches, in both physical and digital spaces, on benchmark datasets, and our newly E-PO dataset (generated with DALL-E 2). Our results show that DiL: i) successfully interprets and quantifies an abnormality's effect on the model's decision-making process, regardless of the abnormality type; and ii) can be leveraged to detect and mitigate this effect.

关键词： Measurement Location awareness computer vision Uncertainty Prevention and mitigation Computational modeling Decision making Object detection Predictive models Object recognition

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3D Part Segmentation via Geometric Aggregation of 2D Visual Features

3D Part Segmentation via Geometric Aggregation of 2D Visual ...

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ieee Workshop on Applications of computer vision (WACV)

作者： Marco Garosi Riccardo Tedoldi Davide Boscaini Massimiliano Mancini Nicu Sebe Fabio Poiesi University of Trento Fondazione Bruno Kessler

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Supervised 3D part segmentation models are tailored for a fixed set of objects and parts, limiting their transferability to open-set, real-world scenarios. Recent works have explored vision-language models (VLMs) as a promising alternative, using multi-view rendering and textual prompting to identify object parts. However, naively applying VLMs in this context introduces several drawbacks, such as the need for meticulous prompt engineering, and fails to leverage the 3D geometric structure of objects. To address these limitations, we propose COPS, a COmprehensive model for Parts Segmentation that blends the semantics extracted from visual concepts and 3D geometry to effectively identify object parts. COPS renders a point cloud from multiple viewpoints, extracts 2D features, projects them back to 3D, and uses a novel geometric-aware feature aggregation procedure to ensure spatial and semantic consistency. Finally, it clusters points into parts and labels them. We demonstrate that COPS is efficient, scalable, and achieves zero-shot state-of-the-art performance across five datasets, covering synthetic and real-world data, texture-less and coloured objects, as well as rigid and non-rigid shapes. The code is available at https://***.

关键词： Point cloud compression Solid modeling Visualization Three-dimensional displays Foundation models Shape Semantics Feature extraction Rendering (computer graphics) Object recognition

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GlobalDoc: A Cross-Modal vision-Language Framework for Real-World Document Image Retrieval and Classification

GlobalDoc: A Cross-Modal Vision-Language Framework for Real-...

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ieee Workshop on Applications of computer vision (WACV)

作者： Souhail Bakkali Sanket Biswas Zuheng Ming Mickaël Coustaty Marçal Rusiñol Oriol Ramos Terrades Josep Lladós L3i La Rochelle Université France Computer Vision Center Universitat Autònoma de Barcelona Spain L2TI Université Sorbonne Paris Nord France AllRead Machine Learning Technologies Spain

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Visual document understanding (VDU) has rapidly advanced with the development of powerful multi-modal language models. However, these models typically require extensive document pre-training data to learn intermediate representations and often suffer a significant performance drop in real-world online industrial settings. A primary issue is their heavy reliance on OCR engines to extract local positional information within document pages, which limits the models' ability to capture global information and hinders their generalizability, flexibility, and robustness. In this paper, we introduce GlobalDoc, a cross modal transformer-based architecture pre-trained in a self supervised manner using three novel pretext objective tasks. GlobalDoc improves the learning of richer semantic concepts by unifying language and visual representations, resulting in more transferable models. For proper evaluation, we also propose two novel document-level downstream VDU tasks, Few-Shot Document Image Classification (DIC) and Content-based Document Image Retrieval (DIR), designed to simulate industrial scenarios more closely. Extensive experimentation has been conducted to demonstrate GlobalDoc's effectiveness in practical settings.

关键词： Representation learning Visualization Image retrieval Semantics Optical character recognition Transformers Robustness Data models Engines Image classification

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A Regional-Level Resource-Saving Model for Winter Road Surface Snow Detection in Extreme Weathers

A Regional-Level Resource-Saving Model for Winter Road Surfa...

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ieee Workshop on Applications of computer vision (WACV)

作者： Xinhao Zhou Tong Wang Zhaodong Liu Hao Wei Guangyuan Pan School of Automation and Electrical Engineering Linyi University Shandong China Department of Civil and Environmental Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Achieving timely and accurate snow detection on road surfaces in extreme weather conditions is vital for both transportation and computer vision applications. However, conventional object detection models, particularly those designed for small targets, fall short in addressing the challenge that is posed by special regional-level multiscale recognition task. To this end, an end-to-end precise and swift road surface snow detection architecture, termed the Resource-Saving Snow Detect Model (RSSD) that includes a multidimensional directional attention mechanism, is proposed. In this model, we designed three dedicated modules, namely Multi-dimensional Bidirectional Attention Module (MDBA), Split-EMA-Convolution (SEC) and Equal Split Convolution (ESC), to address the essential feature extraction and fusion tasks in snow detection. MDBA is able to promote lateral interaction and comprehensive feature fusion across scales, while SEC can not only enhance feature extraction for regional awareness but also reduces computational load, making it efficient under minimal computational power consumption. ESC preserves feature height fusion while significantly reducing computational costs, thereby enhancing the real-time detection capability of the model. In experimental evaluations conducted with data collected by in-vehicle cameras from various roads in the United States and Canada, the results demonstrate higher detection accuracy and speed compared to the latest Transformer-based real-time object detection methods and other exiting methods in the literature. Furthermore, we validated the model's performance and data sensitivity through semi-supervised learning with 50,000 unlabeled images. This research holds significant implications for winter road traffic and provides valuable insights for similar computer vision tasks.

关键词： computer vision Convolution Computational modeling Snow Roads Object detection Feature extraction Transformers Real-time systems Computational efficiency

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SANPO: A Scene Understanding, Accessibility and Human Navigation Dataset

SANPO: A Scene Understanding, Accessibility and Human Naviga...

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ieee Workshop on Applications of computer vision (WACV)

作者： Sagar M. Waghmare Kimberly Wilber Dave Hawkey Xuan Yang Matthew Wilson Stephanie Debats Cattalyya Nuengsigkapian Astuti Sharma Lars Pandikow Huisheng Wang Hartwig Adam Mikhail Sirotenko Google Research Work done while at Google Parallel Domain

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

vision is essential for human navigation. The World Health Organization (WHO) estimates that 43.3 million people were blind in 2020, and this number is projected to reach 61 million by 2050. Modern scene understanding models could empower these people by assisting them with navigation, obstacle avoidance and visual recognition capabilities. The research community needs high quality datasets for both training and evaluation to build these systems. While datasets for autonomous vehicles are abundant, there is a critical gap in datasets tailored for outdoor human navigation. This gap poses a major obstacle to the development of computer vision based Assistive Technologies. To overcome this obstacle, we present SANPO, a large-scale egocentric video dataset designed for dense prediction in outdoor human navigation environments. SANPO contains 701 stereo videos of 30+ seconds captured in diverse real-world outdoor environments across four geographic locations in the USA. Every frame has a high resolution depth map and 112K frames were annotated with temporally consistent dense video panoptic segmentation labels. The dataset also includes 1961 high-quality synthetic videos with pixel accurate depth and panoptic segmentation annotations to balance the noisy real world annotations with the high precision synthetic annotations. SANPO is already publicly available and is being used by mobile applications like Project Guideline to train mobile models that help low-vision users go running outdoors independently. To preserve anonymization during peer review, we will provide a link to our dataset upon acceptance.

关键词： Training computer vision Visualization Navigation Annotations Reviews Computational modeling Semantic segmentation Assistive technologies Synthetic data

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