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.

翻译：最近几天,随着公路上人口和交通的增加,车辆碰撞是全世界死亡的主要原因之一,汽车工业的动力是开发使用传感器的技术,开发计算机愿景领域的先进技术,以建立碰撞探测和碰撞预防系统,协助驾驶员;在本条中,提出了由ResNext建筑和Senet区块组成的深层次学习模型;该模型的性能与VGG16、VGG19、Resnet50和独立Resnext等受欢迎的深层次学习模型相比较;拟议的模型超过了现有基线模型,即使用远小比例的GTACrash合成培训数据,实现0.91的ROC-AUC基准模型,从而减少了计算间接费用。