Research on Traffic Sign Object Detection Algorithm Based on Deep Learning

Traffic mark detection and identification play a key character in the development of driverless and intelligent transportation systems, offering significant assistance in ensuring the safety of people's daily travels. However, the detection effect of traffic signs is affected by many target categories, small targets, and low recognition accuracy, making traffic sign detection more challenging than target detection in general scenarios. In this paper, an improved YOLOv7 network (YOLOv7-COORD) is entered. Foremost, increase CBAM attention module at the connection between backbone and neck network of YOLOv7 to enhance the expression ability of neural networks through the attention mechanism, emphasizing important features and ignoring minor features to enhance the efficiency and precision of the network. Secondly, By adding CoordConv before the upsampling of the neck and before the detection head output, the network can better feel the location message in the characteristic map. Finally, a detection head generated by the low-level, high-resolution characteristic map is added to enhance the recognition accuracy of small target object. The abundance of experimental data demonstrates that the impression of the improved YOLOv7-COORD model is superior to that of the original YOLOv7 model, and the average accuracy of (mAP@0.5) on TT100K datasets is 3.2% higher than that of YOLOv7, reaching 85.4%. In summary, the improved YOLOv7-COORD model can better detect targets in traffic sign images.