Mr. Xinkuang Wang Profile

Xinkuang Wang

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Proceedings Article | 13 June 2024 Paper

A method based on TSM for ego-crash recognition in dashcam videos

Xiantao Chen, Zhongcheng Wu, Xinkuang Wang

Proceedings Volume 13180, 131804T (2024) https://doi.org/10.1117/12.3033681

KEYWORDS: Video, Video surveillance, Feature extraction, Action recognition, 3D modeling

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The scale of traffic accidents is growing, and they can cause massive casualties and property damage. Video data recorded in dashcams is the most important carrier for recording incident moments. Analyzing this video data holds significant importance. Advances in deep convolutional neural networks (CNNs) have significantly propelled the progress of visual accident recognition in the past. Compared to traditional 2D CNNs, 3D CNNs can effectively capture spatial temporal features. However, 3D CNNs suffer from computational issues. To improve the accuracy of 2D CNNs for accident recognition, we incorporate TSMs into 2D CNNs so they can capture more effectively the surface and motion features of traffic accidents in dashcam video data. This enables simultaneous learning of spatial-temporal features in 2D CNNs. We also incorporate coordinate attention into the model, enhancing its capability for spatial-temporal feature learning and improving model performance. Finally, our model achieves higher accuracy in accident recognition than 3D CNNs on a re-organized public traffic video dataset.

Proceedings Article | 8 June 2024 Paper

A lightweight car damage detection algorithm

Qishan Pei, Xinkuang Wang, Zhongcheng Wu

Proceedings Volume 13171, 131710F (2024) https://doi.org/10.1117/12.3031904

KEYWORDS: Damage detection, Object detection

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In response to challenges such as the large number of parameters and high computational demands of vehicle appearance damage detection models, which hinder deployment on mobile devices, this paper presents a study focusing on lightweight and high-precision optimization of the YOLOv5s target detection algorithm. Specifically, we introduce the lightweight network into the YOLOv5s architecture to create a more efficient network. Furthermore, we integrate the attention mechanism to enhance feature extraction capabilities and employ knowledge distillation to improve algorithm accuracy. These enhancements aim to boost target detection performance. The experimental results illustrate that our optimized YOLOv5 algorithm achieves significant improvements in both speed and accuracy on the car damage dataset.

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