Collision Detection: An Improved Deep Learning Approach Using SENet and ResNext
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Abstract (arXiv)
In recent days, with increased population and traffic on roadways, vehicle collision is one of the leading causes of death worldwide. The automotive industry is motivated on developing techniques to use sensors and advancements in the field of computer vision to build collision detection and collision prevention systems to assist drivers. In this article, a deep-learning-based model comprising of ResNext architecture with SENet blocks is proposed. The performance of the model is compared to popular deep learning models like VGG16, VGG19, Resnet50, and stand-alone ResNext. The proposed model outperforms the existing baseline models achieving a ROC-AUC of 0.91 using a significantly less proportion of the GTACrash synthetic data for training, thus reducing the computational overhead.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 13 Jan 2022.
Builds on
17 of 35 references.
References · 17
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5 more
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Citing papers per year: 2026: 1.
1 paper.