Road damage detection algorithm based on GhostNet lightweight YoLov8

The detection of road injuries is crucial for preventing traffic accidents. Given the lack of lightweight in existing road damage detection algorithms, we propose an enhanced Yolov8 algorithm based on GhostNet. This approach effectively reduces the complexity of the network, in terms of both computing requirements and the number of parameters. Moreover, the incorporation of the CBAM attention mechanism module has led to notable enhancements in the accuracy and resilience of the detection process. Furthermore, the F-EIOU Loss is employed to further enhance speed of convergence and positioning accuracy of the model. Results on the RDD2022 dataset demonstrate that our method increases precision, recall, and mAP by 6.4%, 3.9%, and 5.4%, respectively. The parameter size is reduced by approximately 25%, thus confirming that our innovative improvements not only enhance detection accuracy significantly but also notably decrease quantity of parameters in road damage detection.