Research on Leather Defect Detection and Recognition Algorithm Based on Improved Multilayer Perceptron

To address the issues of manual inspection and low precision in the detection and recognition of defects in existing animal leather, this study first establishes a leather image dataset and applies an improved Gabor filtering algorithm for image preprocessing. Specifically, the weighted average method is adopted to grayscale the image, and the algorithm parameters are designed and improved to ensure that most of the key texture information of the leather images is obtained, meeting the requirements for texture feature information in subsequent feature extraction. Next, it explores statistical feature extraction algorithms based on the gray-level co-occurrence matrix and the statistical feature extraction algorithm based on gray-level distribution, forming a combination of features for the dataset. The leather defects mainly include warble fly holes, neck wrinkles, and scars. In the processing process, there are also defects such as scratches, holes, and stains. Finally, a leather defect image classification model is proposed based on a multilayer perceptron algorithm, using the ReLU activation function and a SoftMax classifier to classify surface defects in 1280 samples. The classification time is 0.0854 s, and the average precision, recall, and accuracy for leather defect image classification are all 99.53%. This solution innovatively integrates the improved Gabor filtering with the adaptive multilayer perceptron architecture to construct a multi-modal leather defect classification model, which significantly improves the detection accuracy of three types of defects, namely holes, scratches, and stains. It provides a theoretical reference for the automation of the leather processing process.