In order to solve the problem of insufficient defect feature extraction in the complex mine environment, a fast and intelligent guide defect recognition algorithm RDM-YOLOv5 is proposed by integrating feature enhancement and cascading attention mechanism, aiming to solve the current situation of low efficiency of manual inspection. Firstly, in order to improve the information representation ability of the backbone network feature map, the feature enhancement module RLKM is designed, which enhances the backbone network’s ability to extract target features through reparameterized large kernel convolution, and effectively reduces the amount of model parameters. Then, after extracting the high-level and low-level features through the backbone network, under the action of the cascaded attention mechanism DCAM composed of the designed channel attention mechanism and coordinate attention mechanism, the deep semantic information of the defective target is further mined, and the feature information of the small target are significantly enhanced. Finally, in order to improve the detection accuracy while ensuring the lightweight of the detection network, a lightweight convolution GSConv is introduced into the feature enhancement network to reduce the computational cost while maintaining the accuracy of model detection. The experimental results show that compared with YOLOv5s, the detection accuracy and speed of RDM-YOLOv5 are increased by 3. 7% and 11. 4%, respectively, and the number of model parameters is reduced by 15. 4%. It can basically meet the needs of accurate identification and rapid location of guide surface defects in practical applications.