Smoky vehicle detection based on YOLOv5 and car suppression

Xinran Yu; Xiangquan Chang; Qingcheng Chen

doi:10.1117/12.3004657

9 October 2023 Smoky vehicle detection based on YOLOv5 and car suppression

Xinran Yu, Xiangquan Chang, Qingcheng Chen

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279129 (2023) https://doi.org/10.1117/12.3004657
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

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%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinran Yu, Xiangquan Chang, and Qingcheng Chen "Smoky vehicle detection based on YOLOv5 and car suppression", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279129 (9 October 2023); https://doi.org/10.1117/12.3004657

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KEYWORDS

Object detection

Convolution

Data modeling

Detection and tracking algorithms

Education and training

Visual process modeling

Image classification

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