With the continuous expansion of China’s road network， road disease detection has become an indispensable part of road maintenance and traffic safety， and road disease detection based on deep learning has become a research hotspot in this field. Aiming at the problems of low accuracy and generalization ability of road disease identification in complex scenes with multiple diseases， a road disease detection model called Receptive Ghost Triplet-YOLOv7 （RGT-YOLOv7） in complex scenes is proposed in this paper. A triplet attention mechanism is introduced in the backbone network to improve the correlation of disease features in different channels and spaces， and to solve the problem of low feature extraction efficiency. The original SPP module is replaced by the SPPF module， the Ghost module is added to improve the utilization rate of redundant features， and the original redundant features and the newly extracted features are fused to get more diverse and rich feature information with different scales. In order to improve the model perception field， RFBs module is added in the feature enhancement part， and the feature map is extracted from different directions by using cavity convolution with different sizes to enhance the extraction of horizontal and vertical features. Experimental results show that the average accuracy and balanced F score are improved by 6.9 percentage points and 3.9 percentage points， respectively， compared with YOLOv7， especially the longitudinal fracture identification is improved by 22.3 percentage points， and it also has good performance improvement compared with Faster R-CNN， YOLOv5， and recently proposed algorithm models. It is an effective road disease detection algorithm for proposed RGT-YOLOv7 under complex scenes.