Selective Feature Fusion and Irregular-Aware Network for Pavement Crack Detection

Abstract

Road cracks on highways and main roads are among the most prominent defects. Given the inherent inaccuracy, time-consuming nature, and labor intensiveness of manual road crack detection, there’s a compelling need for automated solutions. The irregular shape of cracks, along with complex background conditions encompassing varying lighting, tree shadows, and dark stains, poses a significant challenge for computer vision-based approaches. Most cracks exhibit irregular edge patterns, which are pivotal features for accurate detection. In response to recent advancements in deep learning within the realm of computer vision, this paper introduces an innovative neural network architecture termed the ‘Selective Feature Fusion and Irregular-Aware Network (SFIAN)’ designed specifically for crack detection on pavements. The proposed network selectively integrates features from multiple levels, enhancing and controlling the flow of valuable information at each stage while effectively modeling irregular crack objects. In an extensive evaluation, this paper conducts experiments on five distinct crack datasets and compares the results with twelve state-of-the-art crack detection methods, including the latest edge detection and semantic segmentation techniques. The experimental findings demonstrate the superior performance of the proposed method, surpassing baseline methods by a notable margin, with an increase of approximately 13.3% in the F1-score, all without introducing additional time complexity. Furthermore, the model achieves real-time processing, achieving a remarkable speed of 35 frames per second (FPS) on images at 320 × 480 pixels, facilitated by NVIDIA 3090 hardware.