An effective graph embedded YOLOv5 model for forest fire detection

Abstract

The existing YOLOv5-based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high-quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small-scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph-embedded YOLOv5 forest fire detection framework, which can improve the performance of small-scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi-head self-attention (MSA) module before each YOLO head. The experimental results on FLAME and our self-built fire dataset show that our proposed model improves the accuracy of small-scale forest fire detection. The proposed model achieves high performance in real-time performance by fully utilizing the advantages of the YOLOv5 framework.