Gale, M.G., Cary, G., Van Dijk, A.I.J.M. and Yebra, M. (2021) Forest Fire Fuel through the Lens of Remote Sensing: Review of Approaches, Challenges and Future Directions in the Remote Sensing of Biotic Determinants of Fire Behaviour. Remote Sensing of Environment, 255. Article ID: 112282.
https://doi.org/10.1016/j.rse.2020.112282

has been cited by the following article:

• TITLE: Forest Fire Smoke Detection Method Based on MoAm-YOLOv4 Algorithm

  AUTHORS: Yihong Zhang, Qin Lin, Changshuai Qin, Hang Ge

  KEYWORDS: Forest Fire Smoke Detection, Pattern Recognition and Intelligent Systems, YOLOv4, Channel Attention Mechanism

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.11, November 1, 2022

  ABSTRACT: To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight network and attention mechanism was proposed. Based on the YOLOv4 algorithm, the backbone network CSPDarknet53 was replaced with a lightweight network MobilenetV1 to reduce the model’s size. An attention mechanism was added to the three channels before the output to increase its ability to extract forest fire smoke effectively. The algorithm used the K-means clustering algorithm to cluster the smoke dataset, and obtained candidate frames that were close to the smoke images; the dataset was expanded to 2000 images by the random flip expansion method to avoid overfitting in training. The experimental results show that the improved YOLOv4 algorithm has excellent detection effect. Its mAP can reach 93.45%, precision can get 93.28%, and the model size is only 45.58 MB. Compared with YOLOv4 algorithm, MoAm-YOLOv4 improves the accuracy by 1.3% and reduces the model size by 80% while sacrificing only 0.27% mAP, showing reasonable practicability.