Improved YOLOX method for small target smoking detection algorithm

Abstract

Smoking is prohibited in many places. In order to detect smoking behaviour and make accurate judgments in a timely manner, machine vision technology is used to carry out research on the detection of small targets of cigarette sticks to improve the problem of difficult and inaccurate recognition of small targets in current detection algorithms and to improve the accuracy of cigarette sticks being recognized. In this study, we propose an improved method to detect smoking behaviour of Yolox based on the attention mechanism, in view of the fact that cigarette sticks occupy a very small part of the image and the presence of hand occlusion. The Attention mechanism module is added to the feature extraction network to focus on local information. Meanwhile, the use of the deep network is increased by adding a scale to focus attention within the target region; and the loss function is optimally replaced and the GIoU loss function is chosen to solve the drawback problem of IoU; experimental results show that the improved Yolox-c algorithm mAP reaches 95.96%, compared with Yolox and Yolov5 mAP, which are improved by 5.87% and 8.85%, respectively.