Lightweight network for millimeter-level concrete crack detection with dense feature connection and dual attention

Abstract

Development of lightweight deep learning crack detection method is essential for the future deployment of mobile device-based structure inspection. The primary challenge involves the analysis and extraction of features from narrow cracks, typically 3–6 pixels wide, which are often obscured by noise such as water stains and shadows. The lightweight model should also maintain high accuracy while ensuring low computational complexity and a minimal number of parameters. To this end, this paper proposes YOLO v5-DE (Dense Feature Enhancement Connection, Efficient and Fast Convolution), a lightweight network based on the YOLO v5 architecture tailored to address these challenges, and constructs crack datasets captured at different heights to investigate the impact of different shooting distances on network performance. The network utilizes efficient convolutions and dense feature connections, with strategic reuse of filtered features from shallow layers, to significantly enhance the model's fine-grained feature information and gradient flow. The experimental results demonstrate that YOLO v5-DE achieves a detection accuracy of 96% for cracks in concrete structures. Compared to the improved YOLO v5 with EfficientViT as the backbone network, YOLO v5-DE achieves 4.7% increase on accuracy while requiring fewer computational resources, with only 1.4 million parameters and 3.6 Giga Floating point Operations Per Second (GFLOPS). Additionally, YOLO v5-DE reduces the inference time to 3.38 ms and increases the frame rate to 295.8 FPS. Moreover, the proposed lightweight network exhibits better detection performance when facing complex backgrounds and real-world environments.