Lightweight forest smoke and fire detection algorithm based on improved YOLOv5

Abstract

Smoke and fire detection technology is a key technology for automatically realizing forest monitoring and forest fire warning. One of the most popular algorithms for object detection tasks is YOLOv5. However, it suffers from some challenges, such as high computational load and limited detection performance. This paper proposes a high-performance lightweight network model for detecting forest smoke and fire based on YOLOv5 to overcome these problems. C3Ghost and Ghost modules are introduced into the Backbone and Neck network to achieve the purpose of reducing network parameters and improving the feature’s expressing performance. Coordinate Attention (CA) module is introduced into the Backbone network to highlight the object’s important information about smoke and fire and to suppress irrelevant background information. In Neck network part, in order to distinguish the importance of different features in feature fusing process, the weight parameter of feature fusion is added which is based on PAN (path aggregation network) structure, which is named PAN-weight. Multiple sets of controlled experiments were conducted to confirm the proposed method’s performance. Compared with YOLOv5s, the proposed method reduced the model size and FLOPs by 44.75% and 47.46% respectively, while increased precision and mAP(mean average precision)@0.5 by 2.53% and 1.16% respectively. The experimental results demonstrated the usefulness and superiority of the proposed method. The core code and dataset required for the experiment are saved in this article at https://github.com/vinchole/zzzccc.git.

Fire and Smoke Detection Using Fine-Tuned YOLOv8 and YOLOv7 Deep Models

“…Table 2 presents some comparative studies with deep learning models for fire and smoke detection. The proposed models outperform the results presented by Yang et al [39]. The authors used a modified YOLOv5 using the same dataset and same number of images; the proposed models provided improvements of 6%, 3.8%, and 5.8% in terms of precision, recall, and mAP:50, respectively.…”

“…The scores presented in Table 1 show that YOLOv8 models reached a precision of 0.954, a recall of 0.848, and a mAP:50 of 0.926. We compared our results with the study in [39]. The authors used the same dataset with YOLOv5 with some modification on the network architecture.…”

“…In our study, we used the dataset proposed by [39]. It consists of 11,667 RGB images, and they were split into training, validation, and test sets at random, with 8494, 2114, and 1059 images in each set.…”