论文标题
碰撞检测：使用SENET和RESNEXT进行了改进的深度学习方法
Collision Detection: An Improved Deep Learning Approach Using SENet and ResNext
论文作者
论文摘要
最近几天，随着道路的人口和交通增加，车辆碰撞是全球死亡的主要原因之一。汽车行业的动机是开发技术来使用传感器和计算机视野领域的进步来构建碰撞检测和碰撞预防系统以协助驾驶员。在本文中，提出了一个基于深度学习的模型，该模型由带有Senet块的Resnext体系结构组成。该模型的性能与流行的深度学习模型（如VGG16，VGG19，Resnet50和独立式Resnext）进行了比较。提出的模型的表现优于现有的基线模型，使用GTACRASH合成数据的比例明显减少了训练的ROC-AUC，从而减少了计算开销。
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.
