Smoky vehicle detection based on YOLOv5 and car suppression

Abstract

Smoky vehicle detection plays an important role in controlling urban pollution and protecting the ecological environment. Due to the complex shape transformation of smoke and the influence of environment, lighting and other factors, recognizing smoke from images is still a major challenge, and there have been many corresponding studies in recent years. Based on the above problems, we collected 2361 pictures of smoky cars as a smoke detection data set using the on-board camera. In addition, we used Yolov5 as the basic model and proposed an improved Yolov5 algorithm by preprocessing and expanding the data. The experimental results show that compared with the initial model, the smoke detection map of the improved algorithm is improved by 1.7%.