Small Target Forest Fire Recognition Method Based on Deep Learning
Due to the small size of fire targets in the early stage of forest fires,which leads to the problem that the model misses the detection of forest fires with small targets,this paper proposes a forest fire identification method based on YOLOv5 for small target detection.In order to improve the detection algorithm for small flames,a new mod-ule is proposed and named CSP-SPPFP(Cross Stage Partial-Spatial Pyramid Pooling Fast Plus),replacing SPP in the YOLOV5 backbone network with CSP-SPPFP.To enhance the representation of flame features,a module based on CBAM attention is proposed.To reduce the information loss during the upsampling of small targets,the original up-sampling method is replaced with transposed convolution.In this paper,one set of experiments is designed to deter-mine which CBAM attention module to use,one set of ablation experiments,and one set of comparison experiments to verify the effectiveness of the proposed algorithm.The experimental results show that the algorithm in this paper im-proves the mAP@50 by 2.12％ compared with the original YOLOv5 algorithm,and can accurately detect forest fire targets for small targets,which effectively improves forest fire prevention capability.
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