Research on Forest Fire Detection Algorithm Based on PL-YOLO

• Published in: 2024 7th International Conference on Mechatronics and Computer Technology Engineering (MCTE) pp. 1318 - 1323
• Main Authors: Tian, Hongwei, Tang, Jian, Zhu, Bohua, Sa, Xingcai, Zhang, Ruiwu, Li, Yu, Li, Zheng, Zhang, Mengdi, Xiu, Yi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.08.2024
• Subjects: Detection algorithms; Feature extraction; forest fire; Forestry; Monitoring; PL-YOLO; real-time; Real-time systems; Robot sensing systems; Robustness; Semantics; Stacking; YOLO
• DOI: 10.1109/MCTE62870.2024.11117258

Aiming at the low efficiency of monitoring forest fire by manual inspection and sensor acquisition, and being easily affected by the environment, a forest fire detection algorithm PL-YOLO based on YOLOv5 is proposed. In order to enhance the ability of model feature extraction and effectively fuse feature maps at different scales, this paper proposes two feature extraction and fusion modules. First, we design the Res2-C3 Block to extract more detailed features, reduce the repetitive semantic information in the network and expand the differences between features. Second, the EUASPP module is constructed to achieve effective fusion and stacking of feature maps of different sampling rates at the same level at the cost of adding a small amount of operations. Finally, the loss function is replaced. The PL- YOLO achieved a precision of 89.65% and a mAP of 87.78% on the dataset we construct. The experimental results show that the proposed algorithm achieves a good detection effect on smoke and flame targets in fire scenes, and it is possible to deploy it on a robot platform to realize real-time automatic forest fire monitoring.