Mingtao Fang’s research while affiliated with Hefei University of Technology and other places

Crack detection is an important task to ensure structural safety. Traditional manual detection is extremely time-consuming and labor-intensive. However, existing deep learning-based methods also commonly suffer from low inference speed and continuous crack interruption. To solve the above problems, a novel bilateral crack detection network (BiCrack) is proposed for real-time crack detection tasks. Specifically, the network fuses two feature branches to achieve the best trade-off between accuracy and speed. A detail branch with a shallow convolutional layer is first designed. It preserves crack detail to the maximum and generates high-resolution features. Meanwhile, the semantic branch with fast-downsampling strategy is used to obtain enough high-level semantic information. Then, a simple pyramid pooling module (SPPM) is proposed to aggregate multi-scale context information with low computational cost. In addition, to enhance feature representation, an attention-based feature fusion module (FFM) is introduced, which uses space and channel attention to generate weights, and then fuses input fusion features with weights. To demonstrate the effectiveness of the proposed method, it was evaluated on 5 challenging datasets and compared with state-of-the-art crack detection methods. Extensive experiments show that BiCrack achieves the best performance in the crack detection task compared to other methods.