Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s

• Published in: PLOS ONE Vol. 19; no. 4; p. e0300502
• Main Authors: Kong, Derui, Li, Yinfeng, Duan, Manzhen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science (PLoS) 18.04.2024; Public Library of Science
• Subjects: Algorithms; Biology and Life Sciences; Coal industry; Computer and Information Sciences; Engineering and Technology; Environmental aspects; Fire detectors; Medicine; Medicine and Health Sciences; Mineral industry; Mining industry; Physical Sciences; Q; R; Research and Analysis Methods; Research Article; Science; Social Sciences; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0300502

Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model’s detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model’s generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.