Pavement Distress Detection Based on Improved YOLOv8n-Ultra Model

To achieve precision and lightweight design for pavement distress detection in complex scenarios, an improved YOLOv8n model, named YOLOv8n-Ultra, is constructed. The Coordinate Attention (CA) module is embedded into the C2f layer of the backbone network to empower the feature extraction of the neural network to focus on specific semantic information related to distress. The Ghost module is introduced to realize lightweight design of the model, and the Wise Intersection over Union (WIoU) loss function is adopted to dynamically optimize the precision of bounding box regression, enabling the model to pay more attention to hard-to-detect objects. Ablation experiments are designed to test the impact of different improvement methods on the detection performance of the model. Verified by three repeated experiments, the results show that compared with the YOLOv8n model, the YOLOv8n-Ultra model improves the precision (P) from 78.5% to 79.4%, increases the recall (R) from 74.0% to 78.7%, and enhances the mAP0.5 by 3.8 percentage points to 82.7%. It only increases the parameter count by 65.1% to 4.97 M, which is still substantially lower than that of traditional models such as YOLOv3 (61.92 M) and Faster Region-based Convolutional Neural Network (Faster-RCNN, 107.5 M), while maintaining an FPS of 202.4 f/s when tested on the experimental hardware (NVIDIA GeForce RTX 2060 SUPER GPU) specified in Section “Experimental Environment and Parameter Settings”. A paired t-test (p < 0.05) confirms that the improvement effect is statistically significant and the model exhibits good stability. In summary, the YOLOv8n-Ultra model provides a technical reference for pavement distress detection with balanced precision and lightweight characteristics.