Enhanced and improved garbage identification and classification of YOLOV5 based on data

Abstract

In the current social background, the problem of garbage classification highlights its importance, and intelligent solutions are urgently needed. This research focuses on the study of a convolutional neural network-based garbage sorting robot detection algorithm model to effectively address this challenge. By using data enhancement technology, this study processed garbage images to improve the adaptability and robustness of the model. The adversarial sample generation technology is introduced to further enhance the ability of the model to resist noise and attack. Combined with YOLOV5 and the CA attention mechanism, the model performs well in garbage sorting tasks. In the training and testing of 3700 images, the garbage classification recognition accuracy rate reached 96.70%. This study not only provides intelligent and efficient solutions for the field of garbage sorting but also highlights the value of scientific literacy in practical problem solving.