Affiliations: [a] Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China | [b] Department of Computer Engineering, Chonnam National University, Gwangju 61186, Korea
Abstract: Traffic sign detection is a challenging task. Although existing deep learning techniques have made great progress in detecting traffic signs, there are still many unsolved challenges. We propose a novel traffic sign detection network named ReYOLO that learns rich contextual information and senses scale variations to efficiently detect small and ambiguous traffic signs in the wild. Specifically, we first replace the conventional convolutional block with modules that are built by structural reparameterization methods and are embedded into bigger structures, thus decoupling the training structures and the inference structures using parameter transformation, and allowing the model to learn more effective features. We then design a novel weighting mechanism which can be embedded into a feature pyramid to exploit foreground features at different scales to narrow the semantic gap between multiple scales. To fully evaluate the proposed method, we conduct experiments on a traditional traffic sign dataset GTSDB as well as two new traffic sign datasets TT100K and CCTSDB2021, achieving 97.2%, 68.3% and 83.9% mAP (Mean Average Precision) for the three-class detection challenge in these three datasets.
Keywords: Network reparameterization, features adaptive weighting, traffic sign detection, deep learning
