A Novel Search Strategy-Based Deep Learning for City Bridge Cracks Detection in Urban Planning

City bridge crack detection is an important problem in the field of image processing, which is very important for city road planning and development. Aiming at the problem that traditional bridge cracks detection algorithms cannot satisfy the requirements of high precision and high efficiency at the same time, this paper proposes a new deep learning method based on search strategy for city bridge cracks detection. Firstly, the sliding window algorithm is used to divide the bridge cracks image into smaller bridge crack meta-image and bridge background meta-image. Based on the analysis of the meta-image, a city bridge cracks detection network (CBCDN) model is proposed. CBCDN is used to identify bridge background surface elements and bridge crack surface elements. Then, the CBCDN based on the new window sliding algorithm is used to detect the bridge cracks. Finally, a novel search strategy is adopted to accelerate the CBCDN. The experimental results show that the proposed algorithm has a better detection effect and stronger generalization ability compared with the traditional algorithms. We also make a comparison with the state-of-the-art bridge cracks detection models; the results show that the proposed network model can better detect the bridge cracks in the city scene, and it can obtain a better effect in terms of objective and subjective evaluation.