A lightweight road defect detection method based on multi-scale hybrid feature fusion

Abstract

Automatic road defect detection plays a significant role in road upkeep and transportation safety. However, existing approaches still have some shortcomings in detection accuracy, real-time, and hardware requirement. In this paper, we propose a novel anchor-free road defect detection method based on multi-scale hybrid feature fusion. First, we design a lightweight first-order detector to keep more semantic features. Then, we employ a depth separable convolutional layer to reduce the computational complexity. Finally, we propose a hybrid feature fusion framework to improve the feature description capability. Rigorous experimental evaluations on road benchmark data sets demonstrate that our method achieves the highest accuracy and outperforms the YOLO series models. Furthermore, our method has a short inference time of 32ms, which makes it an excellent model in real-time defect detection tasks.