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%.
Highlights
W ITH the development of the driver-assistance system and autonomous vehicle, the Traffic Sign Detection (TSD) system has been heavily studied over the past decade
The IFA-Feature Pyramid Network (FPN) is designed based on the following three ideas: 1) We found that deep pyramid levels play a minor role in traffic sign detection and we remove deep layers for reducing inference time
When our proposed IFA-FPN is applied to the Cascade RCNN, the best performances are achieved by 80.3%
Summary
W ITH the development of the driver-assistance system and autonomous vehicle, the Traffic Sign Detection (TSD) system has been heavily studied over the past decade. To design a more suitable neck network for traffic sign detection methods, we analyze the statistical characteristics of traffic signs including the size distribution and the usage of pyramid levels P2-P5 in Region-of-Interest (RoI) Alignment step. The IFA-FPN is designed based on the following three ideas: 1) We found that deep pyramid levels play a minor role in traffic sign detection and we remove deep layers for reducing inference time. In IFA-FPN, the P2 need to represent features of all size of traffic signs because of the proposed integrated operation. The contributions of this work are summarized as follows: 1) To overcome the size and class imbalance problem of traffic signs, we proposed an IO which integrates all scale RoIs into a certain pyramid level.
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