Improved Faster-RCNN Algorithm for Traffic Sign Detection

This article proposes an improved Faster-RCNN algorithm for detecting small traffic signs, which addresses the issues of poor recognition performance of distant small targets and high computation cost in real-world traffic scenes affected by weather and lighting conditions. Based on the basic architecture of Faster-RCNN, the algorithm reconstructs the backbone network and improves the region proposal network to make the network framework lightweight. A multi-scale feature fusion network is designed by integrating the scSE attention and GSConv modules, and the Anchors box size is updated to improve the localization and recognition of traffic sign targets. The ROI Align pooling operation with bilinear interpolation for each target subregion is used to preserve the detailed features of the target region and improve the ability to capture details of distant targets. The balanced L1 loss function is adopted to address the problem of imbalance between samples with large gradient difficulty and those with small gradient easiness, thus improving the training effect. Experiments were conducted on the expanded TT100K dataset. Results show that compared with traditional Faster-RCNN, the model weight is reduced by 200 MB, and detection accuracy is improved by . The algorithm achieves a detection accuracy of in low-intensity environments such as cloudy days, which helps improve the traffic sign detection performance in extreme environments.