ABSTRACT
An improved Ghost-YOLOv5s detection algorithm is proposed in this paper to solve the problems of high computational load and undesirable recognition rate in the traditional detection methods of pavement diseases. Ghost modules and C3Ghost are introduced into the YOLOv5s network to reduce the FLOPs (floating-point operations) in the feature channel fusion process. Mosaic data augmentation is also added to improve the feature expression performance. A public road disease dataset is reconstructed to verify the performance of the proposed method. The proposed model is trained and deployed to NVIDIA Jetson Nano for the experiment, and the results show that the average accuracy of the proposed model reaches 88.17%, increased by 4.01%, and the model FPS (frames per second) reaches 12.51, increased by 184% compared with the existing YOLOv5s. Case studies show that the proposed method satisfies the practical application requirements of pavement disease detection.
