Integrated Feature Pyramid Network With Feature Aggregation for Traffic Sign Detection

Abstract

Traffic sign detection is a critical task in the visual system of the Advanced Driver Assistance System (ADAS) and the Automated Driving System (ADS). Although the general object detection has achieved promising results by using Feature Pyramid Network (FPN) in recent years, we still observed that FPN cannot obtain satisfactory results in traffic sign detection because the size and class distribution of traffic signs are extremely unbalanced. To overcome this problem, a novel Plug-and-Play neck network Integrated Feature Pyramid Network with Feature Aggregation (IFA-FPN) is proposed in this paper based on the statistical characteristics of traffic signs. First, a lightweight operation is introduced to fully utilize the model and improve the inference speed of the model. Second, an Integrated Operation (IO) is introduced to solve the imbalance problem of Region-of-Interests (RoIs) in pyramid levels. Third, we introduce a Feature Aggregation (FA) structure to strengthen the feature representation capacity of feature maps, thereby enhancing the network robustness against the size discrepancy of traffic signs. The experiments are performed on three mainstream datasets, i.e., the German Traffic Sign Detection Benchmark (GTSDB), Swedish Traffic Sign Dataset (STSD), and Tsinghua-Tencent 100k dataset (TT100k). The experimental results demonstrate the superiority of the proposed IFA-FPN in the traffic sign detection tasks. Specifically, when the proposed IFA-FPN is applied to the Cascade RCNN, it achieves 80.3% mAP in GTSDB which surpasses FPN by 9.9%, 65.2% in mAP in STSD which surpasses FPN by 3.5%, and 93.6% in mAP in TT100k which surpasses FPN by 1.6%.

Keywords

• Automated driving system
• driver assistance system
• feature aggregation
• small object detection
• traffic sign detection