Intelligent detection and recognition of road cracks based on improved YOLOV8

Deep learning plays a vital role in road crack detection, enabling improved detection accuracy, reduced costs, and facilitated automated maintenance, thus enhancing road safety and traffic efficiency. However, most of their remarkable performance relies on complex and costly computational resources, which often cannot meet the requirements for both speed and accuracy in mobile deployment terminals. In this paper, to address the trade-off between high accuracy and real-time performance, an efficient YOLOv8-improved network is proposed. This network not only reduces network redundancy but also significantly improves inference speed, achieving a balance between high accuracy and real-time performance. This paper employs LAMP pruning techniques to optimize the model as the student model in knowledge distillation, and further designs a teacher network that integrates the BAM attention module, C2f-DynamicConv, and CARAFE upsampling operator to provide feature knowledge distillation for the pruned model. The BAM module enhances the network's sensitivity to critical information, C2f-DynamicConv expands the receptive field to enhance feature extraction capabilities, and CARAFE, based on content-adaptive upsampling, aggregates contextual information to provide richer features for prediction tasks. Experimental data shows that our model achieves a significant 69.9% improvement in FPS and a 3.98% increase in map@50 accuracy.