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Due to the complexity and variety of textures on Strip steel, it is very difficult to detect defects on rigid surfaces. This paper proposes a metal surface defect classification method based on an improved bat algorithm to optimize BP neural network. First, this paper uses the Local Binary Pattern(LBP) algorithm to extract features from six types of defect images including inclusion, patches, crazing, pitted, rolled-in, and scratches, and build a feature sample library with the extracted feature values. Then, the WG-BA-BP network is used to classify the defect images with different characteristics. The weighted experience factor added by the network can control the flight speed of the bat according to the number of iterations and the change of the fitness function. And the gamma distribution is added in the process of calculating loudness, which enhances the local searchability. The BP network optimized by this method has higher accuracy. Finally, to verify the effectiveness of the method, this article introduces the five evaluation indicators of accuracy, precision, sensitivity, specificity, and F1 value under the multi-class model. To prove that this algorithm is more feasible and effective compared with other swarm intelligence algorithms. The best prediction performance of WG-BA-BP is 0.010905, and the accuracy rate can reach 0.9737.

The surface inspection of strip steel defects plays a vital role in the industry, and it has attracted widespread attention in the industry. In this paper, an improved sparrow search algorithm (WMR-SSA) with intelligent weighting factors and mutation operators is proposed, WMR-SSA can balance the development capability of the algorithm based on the number of iterations. In addition, WMR-SSA enhances the local search capability of the algorithm through mutation operators. At the same time, the algorithm determines the initial position of the population by random walk to enhance the diversity of the population. The WMR-SSA algorithm is compared with GA, PSO, CS, GWO, BSA, and original SSA, and the experiment proves that the WMR-SSA algorithm is better than other algorithms. In this study, WMR-SSA is combined with BP neural network and implemented for the classification of defective strip images. The accuracy and stability of WMR-SSA-BP are effectively demonstrated experimentally by comparing it with classifiers optimized by other intelligent algorithms.

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