Detection of Road Objects With Small Appearance in Images for Autonomous Driving in Various Traffic Situations Using a Deep Learning Based Approach
Guofa Li, Heng Xie, Weiquan Yan, Yunlong Chang, Xingda Qu
Abstract
Effectively detecting road objects in various environments would significantly improve driving safety for autonomous vehicles. However, small objects, low illumination, and blurred outline in images strongly limit the performance of current road object detection methods. To solve these problems, this paper proposed a novel deep learning anchor-free approach based on CenterNet. The atrous spatial pyramid pooling (ASPP) was used to extract features from multiple scales to improve the detection performance while not increasing the computational cost and the number of parameters. The space to depth algorithm was then adopted in our proposed approach to optimize the traditional downsampling process. A large-scale naturalistic driving dataset (BDD100K) was used to examined the effectiveness of our proposed approach. The experimental results show that our proposed approach can effectively improve the detection performance on small objects in various traffic situations.