Abstract: To accurately classify materials and improve industrial production capacity, a visual image enhanced automatic target recognition algorithm for material conveyor belts is proposed. Using a single scale algorithm to process the collected visual images. Image parameters are adjusted through Gaussian wrapping function, and restoring the optimal color of the visual image based on color constancy theory, ensuring the original color characteristics, completing color channel indexing based on image feature conditions, and enhancing the target visual image of the material conveyor belt. The Laplace operator is used to improve the scale space of the image, determine the extreme value points of feature pixels, establish a function to fit and recognize the strong edge response of the target, describe the distribution of key points of the target pixel through a histogram, obtain image gradient features and angle features, and determine the contour response value of the transmitted material through a sliding window. The features are trained to complete automatic target recognition. The experimental results show that the proposed method can effectively identify material targets on the conveyor belt, with high recognition accuracy and strong practicality. 

Key words: visual images, image enhancement, material conveyor belt, target recognition algorithm, scale space, feature extraction