Application of Computer Vision Algorithms to Solve the Problem of Smoke Detection in Industrial Production

This paper proposes an approach for detecting smoke in industrial production using computer vision. The task of detecting smoke and fire can be framed as a detection problem, making modern convolutional neural network models well-suited for this task. The main issues of detection in industrial production are considered, and solutions to these problems are proposed. In the study, the Faster R-CNN, MobileNet SSD v2, and YOLOv8 models were trained and tested in combination with various image preprocessing algorithms. The best result was achieved by the YOLOv8 model combined with the adaptive histogram equalization algorithm for image preprocessing, showing a precision value of 80.1%. As a result, it was demonstrated that deep convolutional networks are well-suited for the task of detecting smoke and fire. Additionally, the main problems and solutions for preparing data for training deep convolutional models were explored.