Collision Detection: An Improved Deep Learning Approach Using SENet and ResNext
This work addresses collision prevention for drivers, but it is incremental as it builds on existing architectures.
The paper tackled vehicle collision detection by proposing a deep learning model combining ResNext and SENet, which achieved a ROC-AUC of 0.91 and reduced computational overhead by using less training data.
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.