Machine vision-based conveyor belt tear detection in a harsh environment

Abstract

Abstract This work proposes a recognition method to improve the visual detection rate of longitudinal conveyor belt tears related to fog pollution and uneven illumination. It integrates the Haar feature with a modified dark channel defogging algorithm. Firstly, the dark channel algorithm was used to eliminate fog interference with belt images, and the processing time was reduced from 14.5 ms to 5 ms by calculating a single necessary image channel. The Haar feature was used to replace the traditional geometric features due to its high-efficiency feature expression and illumination robustness; this provided a strong feature basis for the learning and training of the classifiers. Next, the classifiers were trained and strengthened by the AdaBoost algorithm, and the strengthened classifiers were cascaded by the Cascade algorithm to improve the recognition ability. Finally, experiments were carried out under mixed fog pollution and uneven illumination. The results show that the average accuracy, recall, precision and false positive rate of the proposed method are approximately 98.4%, 96.9%, 99.9% and 0.1%, respectively, while the recognition period was only 52.3 ms. This shows strong adaptability and robustness to a harsh environment and demonstrates that the proposed method has important application value.