CrackYOLO: towards efficient dam crack detection for underwater scenes

Abstract

Crack is one of the main factors threatening the safety of the dam. Automatic image object detection is the main way of underwater dam crack detection. However, the traditional methods have problems with low crack detection speed, high false alarm rate, and poor robustness. In addition, the existing methods cannot get a satsifying detection result with a high detection speed. To solve these problems, we propose an efficient dam crack detection method for underwater scenes, called CrackYOLO. Firstly, to better integrate the multi-scale features without incurring excessive computational costs, we propose a feature fusion module in CrackYOLO. Next, we re-design the skip-connection in the network to get better features, compressing the overall model parameters. Then, we propose a feature extraction module called Res2C3, which combines semantic and location information. After that, we proposed a BCAtt to make features focus on both channel and location information. Finally, according to the characteristics of dam underwater crack images, we use a genetic algorithm to select the best value of hyperparameters of the model. The experimental results show that the proposed method detects underwater dam cracks robustly with less computational cost. Our CrackYOLO can get 94.3% mAP[0.5] and 151 FPS in underwater crack detection task which can achieve a real-time detection in practice.