Crack detection of continuous casting slab by evolutionary topology backbone search

In recent years, computer vision-based methods have been widely used in steel defect detection. Traditional image detection methods mainly rely on manually extracted features, resulting in poor generalization. Deep learning methods are sensitive to the number of samples, and the network structure design relies heavily on manual experience. To address these problems, a backbone network search algorithm based on evolutionary topology is proposed in this paper for crack detection on continuous casting surfaces. Firstly, a variable-length genetic encoding scheme is designed for industrial defect problems with different data complexity, which can improve the applicability of the algorithm and extend the search space. Secondly, to effectively solve the channel redundancy problem in densely connected CNNs, a random pruning strategy for network connection channels is proposed to reduce the topological space and the complexity of the model. Finally, a computational resource allocation mechanism based on a dynamic surrogate model is devised. The surrogate model predicts the individual performance to ensure that computational resources can be concentrated on individuals with better quality. In addition to the steel crack image dataset, the proposed method also uses the workpiece crack image dataset for a supplementary experiment. Experimental results show that the proposed algorithm can achieve better detection performance with fewer computational resources compared to manually designed deep learning algorithms and classical approaches that use evolutionary algorithms to search network architectures.