| Title |
Development of a Computer Vision-based Monitoring System for Enhanced Industrial Waste Incineration Management |
| Authors |
(Lan Zhu) ; (Deqiang Fei) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.3.309 |
| Keywords |
Computer vision; Industrial waste; Incineration treatment; Image recognition; Image segmentation |
| Abstract |
In modern industrial production, waste incineration treatment is a key link, but the monitoring and management of this process have a significant impact on the environment and resource utilization. To improve the efficiency and environmental safety of incineration treatment, this study aims to design and develop a computer vision-based monitoring software for industrial waste incineration treatment. This study uses a high-temperature resistant pinhole lens as a monitoring device, uses a U-Net algorithm to detect the combustion adequacy of garbage incineration, and uses an image segmentation algorithm to monitor the garbage incineration situation in real-time.
The flame image information is collected through computer vision technology and the flame morphology inside the incinerator is analyzed. In addition, a computer vision-based waste incineration monitoring system has been developed for the incineration process of industrial waste. Image processing technology and deep learning algorithms are used to monitor and analyze the incineration process in real-time. SimAM attention module is added based on the original YOLOv5 architecture to improve the model’s ability to recognize flame image features. The results showed that in the training and validation sets, the recognition accuracy of the computer vision-based system was 94.8% and 94.1%, respectively. The improved YOLOv5 algorithm achieved an AUC value of 0.987, a mAP0.5 value of 0.877, and an accuracy of 95.8%. This indicates that the development and design of industrial waste incineration monitoring software based on computer vision significantly improves treatment efficiency and accuracy, while effectively reducing human errors and omissions. |