Spatial Pyramid Pooling and Adaptively Feature Fusion based Yolov3 for Traffic Sign Detection

Abstract

Traffic sign detection is a key part of intelligent assisted driving, but also a challenging task due to the small size and different scales of objects in foreground and closed range. In this paper, we propose a new traffic sign detection scheme: Spatial Pyramid Pooling and Adaptively Spatial Feature Fusion based Yolov3 (SPP and ASFF-Yolov3). In order to integrate the target detail features and environment context features in the feature extraction stage of Yolov3 network, the Spatial Pyramid Pooling module is introduced into the pyramid network of Yolov3. Additionally, Adaptively Spatial Feature Fusion module is added to the target detection phase of the pyramid network of Yolov3 to avoid the interference of different scale features with the process of gradient calculation. Experimental results show the effectiveness of the proposed SPP and ASFF-Yolov3 network, which achieves better detection results than the original Yolov3 network. It can archive real-time inference speed despite inferior to the original Yolov3 network. The proposed scheme will add an option to the solutions of traffic sign detection with real-time inference speed and effective detection results.