Research on Detection of Multiple Types of Speed Bump Defects Based on CRSCCG-YOLOv5s

Speed bumps, as a crucial road safety infrastructure, can directly impact the accident rates on specific road sections if they have defects. Therefore, this study proposes a method for detecting speed bump multiclass defects using an improved YOLOv5s model. This model modifies the pooling method in the backbone layer to Spatial Pyramid Pooling Fast (SimSPPF) to enhance the detection speed while improving the feature extraction capability from images. In the neck layer, firstly, the upsampling method was changed to Convolutional Transpose (ConvTranspose) to increase the accuracy of small target detection. Then, the Contextual operation network (CotNet) module was integrated with the original C3 module, enhancing the model’s ability to recognize different defects by obtaining better global features. Finally, the convolution module was restructured to a Recursive Gated Convolution (gnconv) module, designed to maximize the model’s capacity to capture complex multi-scale, multiclass image features. Additionally, a new data augmentation method (CR) was proposed for data enhancement and balancing of the samples. Experimental results show that the improved YOLOv5s algorithm achieved an accuracy of 97.7%, a recall rate of 91.9%, and a mean precision of 96.4% while maintaining a parameter size of only 8.8M. Compared to other YOLO detection models, the improved model exhibits high accuracy, high confidence, and a low parameter count.