Focused Feature Enhancement and Multi-scale Feature Fusion for Road Defect Detection

Given the characteristics of road defect detection, such as the small proportion of defective targets in defective images, the large scale difference of different types of defects, and the complex background environment, as well as the limitations of current methods, which are unsuitable for edge deployment and real-time detection, we propose a road defect detection model with focused feature enhancement and multi-scale feature fusion. Firstly, we create a Global-Local Feature Extractor (GLFE) that extracts global and local feature information from road defect images, allowing us to adapt to the vast span and slender features of crack objects while also improving feature extraction capacity for low resolution images. Secondly, we propose an Adaptive-Generalized Feature Pyramid Network (Adaptive-GFPN) based on contextual information as the model's feature fusion network, as well as design and introduce a Context Aware Fusion Module (CAFM) and a dynamic up-sampling operation (DYsample) that can be guided and adapted based on the contextual information of features at different scales to improve defect detection accuracy in complex backgrounds. Finally, to improve detection efficiency, we design a Lightweight Detection Head (LDH) that reduces the number of network parameters and computations. The experimental results reveal that, when compared to the baseline model YOLOv8, our model improved [email protected] by 5.2%, Precision by 3.5%, and Recall by 4.5% on the RDD2022 dataset.