LTF-YOLO: an intelligent road defect detection model based on large-kernel enhancement and local multi-scale feature...

To address the inefficiency and high cost of manual road inspections and the limited accuracy of existing computer vision methods, this study proposes a multi-scale road defect detection model, LTF-YOLO, based on YOLO11s. The model aims to enhance both the accuracy and robustness of automated road defect detection.

In the feature-extraction stage, an LMSFA module replaces the original C3k2 module in YOLO11s. This module employs a hierarchical large-kernel convolution combined with group convolution and block-concatenation strategies to achieve efficient multi-scale feature aggregation. During feature fusion, an improved SPPFMPF module introduces average pooling and channel attention branches, with learnable weights enabling adaptive fusion of multi-source features. In the detection head, the TADDH module separates classification and regression subspaces through a task-decoupling mechanism. The regression branch incorporates DyDCNv2 dynamic convolution for spatial adaptive alignment, while a confidence-guided mechanism enhances detection reliability.

Experiments on the SVRDD dataset demonstrate that, compared with the baseline YOLO11s, LTF-YOLO improves Precision by 4.4%, rising from 70.2 to 74.6, mAP@50 by 3.3%, improving from 66.4 to 69.7, and mAP@50-95 by 3.8%, rising from 39.4 to 43.2. Cross-dataset experiments on the RDD2022 dataset further demonstrate the strong generalization ability of LTF-YOLO, with mAP@50 increasing from 47.2 to 49.2 while maintaining a lightweight architecture. The proposed model achieves more accurate identification of cracks and potholes under complex road conditions, effectively reducing missed and false detections.

Through innovations in multi-scale feature extraction, adaptive feature fusion, and task-decoupled detection head design, LTF-YOLO significantly enhances detection accuracy and robustness. The model maintains high precision while remaining lightweight, providing an efficient and scalable solution for intelligent road maintenance and traffic safety monitoring.