基于改进YOLOv5s的森林烟火检测算法研究OA

This paper proposes an improved forest fire detection algorithm based on YOLOv5s.The algorithm enhances the original YOLOv5s model by introducing the GSConv lightweight convolution and a strategy to eliminate grid sensitivity.Extensive experiments are conducted on a forest fire dataset,and the proposed algorithm is successfully deployed on a drone for real-world testing.The experimental results demonstrate significant performance improvements achieved by the enhanced model in forest fire detection.The average accuracy of the model is 90.65%,and the detection time is only 4.1 ms,which meets the high precision and real-time requirements of pyrotechnic detection.This study provides a strong support for the practical application of forest fire detection algorithm,and has important practical significance and application value.