基于改进YOLOv5的火焰检测算法

IF 3 3区 农林科学 Q2 ECOLOGY Fire-Switzerland Pub Date : 2023-08-13 DOI:10.3390/fire6080313
Xingang Xie, Ke Chen, Yiran Guo, Botao Tan, Lumeng Chen, Min Huang
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引用次数: 1

摘要

火焰识别是消防中的一项重要技术,但现有的图像火焰检测方法速度慢,精度低,不能准确识别小火焰区域。目前的探测技术难以满足消防无人机在火灾现场的实时探测需求。为了改善这种情况,我们开发了一种基于yolov5的实时火焰检测算法。该算法能够快速准确地检测出火焰。主要改进有:(1)嵌入的坐标注意机制有助于模型更精确地发现和检测感兴趣的目标。(2)改进了小目标检测层,增强了模型的关联识别能力。(3)引入了新的损失函数α-IoU,提高了回归结果的准确性。(4)将模型与迁移学习相结合,提高模型的准确性。实验结果表明,增强后的YOLOv5的mAP可以达到96.6%,比原始的mAP提高了5.4%。该模型识别单幅图像的时间为0.0177 s,证明了其效率。综上所述,改进后的YOLOv5网络模型的整体效率优于原始算法和现有主流识别方法。
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A Flame-Detection Algorithm Using the Improved YOLOv5
Flame recognition is an important technique in firefighting, but existing image flame-detection methods are slow, low in accuracy, and cannot accurately identify small flame areas. Current detection technology struggles to satisfy the real-time detection requirements of firefighting drones at fire scenes. To improve this situation, we developed a YOLOv5-based real-time flame-detection algorithm. This algorithm can detect flames quickly and accurately. The main improvements are: (1) The embedded coordinate attention mechanism helps the model more precisely find and detect the target of interest. (2) We advanced the detection layer for small targets to enhance the model’s associated identification ability. (3) We introduced a novel loss function, α-IoU, and improved the accuracy of the regression results. (4) We combined the model with transfer learning to improve its accuracy. The experimental results indicate that the enhanced YOLOv5′s mAP can reach 96.6%, 5.4% higher than the original. The model needed 0.0177 s to identify a single image, demonstrating its efficiency. In summary, the enhanced YOLOv5 network model’s overall efficiency is superior to that of the original algorithm and existing mainstream identification approaches.
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来源期刊
Fire-Switzerland
Fire-Switzerland Multiple-
CiteScore
3.10
自引率
15.60%
发文量
182
审稿时长
11 weeks
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