A Lightweight Road Crack Detection Model Based on YOLOv8
Road cracks are one of the main pavement diseases,and timely and effective detection of road cracks is crucial for pavement maintenance and management.To reduce the model parameter size,improve infer-ence speed,a lightweight road crack detection model based on improved YOLOv8 is proposed.This model embeds a lightweight module called FasterNet into the Neck layer of the YOLOv8 network,that reducing redundant compu-tations and memory access while effectively extracting spatial features.Experimental verification of the algorithm's detection performance is conducted on a self-made road crack dataset.The results show that the improved model significantly reduces model parameters and computational complexity,while also improving Recall and MAP to a certain extent.It ensures the accuracy of road crack detection and facilitates deployment on embedded devices.
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