计算机工程与应用2024，Vol.60Issue(24)：340-350,11.DOI:10.3778/j.issn.1002-8331.2308-0349
改进YOLOv7的无明火森林烟雾检测算法
Forest Smoke Detection Method Without Open Flames Based on Improved YOLOv7
摘要
Abstract
Rapid and accurate judgment of forest fire is of great significance to forest fire prevention.However,the existing forest smoke detection model extracts a single smoke feature.Therefore,the existing models do not perform well in the fire detection task when there is only smoke in the image with no visible fire.To address this problem,an improved YOLOv7-based smoke detection algorithm for forests without open fires is proposed.The algorithm introduces the atten-tion mechanism CA and the full convolutional mask self-encoder framework FCMAE in the backbone network,so that the model can obtain richer local information while extracting semantic features and solves the feature collapse problem existing in the existing model.Meanwhile,a centralized feature pyramid CFP is introduced into the prediction network to strengthen the intra-layer adjustment ability of features.In addition,the model uses the loss function Wise-IoU with dynamic non-monotonic FM to strengthen the detection ability of low-quality smoke samples.The experimental results show that compared to other models,this model performs better in detecting smoke without open flames,with an accuracy of 98.1%,mAP@50%reaching 99.1%.关键词
无明火森林烟雾检测/YOLOv7/注意力机制/特征金字塔Key words
forest smoke detection without open flames/YOLOv7/attention mechanism/feature pyramid分类
信息技术与安全科学引用本文复制引用
王灏文,朴燕,王鈅,姜品依..改进YOLOv7的无明火森林烟雾检测算法[J].计算机工程与应用,2024,60(24):340-350,11.基金项目
吉林省自然科学基金(20210101180JC). （20210101180JC）
