Strip surface defects have large intraclass and small interclass differences, resulting in the available detection techniques having either a low accuracy or very poor real-time performance. In order to improve the ability for capturing steel surface defects, the context fusion structure introduces the local information of the shallow layer and the semantic information of the deep layer into multiscale feature maps. In addition, for filtering the semantic conflicts and redundancies arising from context fusion, a feature refinement module is introduced in our method, which further improves the detection accuracy. Our experimental results show that this significantly improved the performance. In particular, our method achieved 79.5% mAP and 71 FPS on the public NEU-DET dataset. This means that our method had a higher detection accuracy compared to other techniques.

In this work, we propose a new crack image detection and segmentation method for addressing the issues regarding the poor detection of crack structures in certain complex background conditions, such as the light and shadow, and the easy-to-lose details in segmentation. This method can be categorized into two phases, where the first one is the coding phase. In this phase, the channel attention mechanism and crack characteristics, using the correlation channel with different scales increasing the network robustness and ability of feature extraction, have been introduced to decouple the channel dimension and space dimension. It also avoids underfitting caused by information redundancy during the jumping connection. In the second stage, i.e., the decoding stage, the spatial attention mechanism has been introduced to capture the crack edge information through the global maximum pooling and global average pooling of the high-dimensional features. Then, the correlation between the space and channel has been recovered through multiscale image information fusion to achieve accurate crack positioning. Furthermore, the Dice loss function has been employed to solve the problem of pixel imbalance between the categories. Finally, the proposed method has been tested and compared with existing methods. The experimental results illustrate that our method has a higher crack segmentation accuracy than existing methods. Furthermore, the mean intersection over the union ratio reaches 87.2% on the public dataset and 83.9% on the self-built dataset, and it has a better segmentation effect and richer details. It can solve the problem of crack image detection and segmentation under a complex background.