基于改进YOLOV5的火焰检测算法

摘要/Abstract

摘要： 针对现有的火焰检测算法检测平均精度低、小目标火焰漏检率高的问题，提出一种改进YOLOV5的火焰检测算法。该算法使用Transformer Encode模块代替YOLOV5主干网络末端的CSP bottleneck模块，以增强网络捕获不同局部信息的能力，提高火焰检测的平均精度，并且在YOLOV5网络中增加CBAM注意力模块，增强网络提取图像特征的能力，对于小目标火焰能够较好地提取特征，降低小目标火焰的漏检率。将该算法在公开数据集BoWFire、Bilkent上进行实验，结果表明，改进YOLOV5网络的火焰检测平均精度更高，可达83.9%，小目标火焰漏检率更低，仅为1.6%，检测速率为34帧/s，相比于原YOLOV5网络，平均精度提升了2.4个百分点，小目标火焰漏检率降低了4.1个百分点，改进后的YOLOV5网络能够满足火焰检测的实时性和精度要求。

关键词: YOLOV5算法, Transformer, CBAM注意力

Abstract: Aiming at the existing flame detection algorithms having problems of low average detection accuracy and high missed detection rate of small target flames， an improved YOLOV5 flame detection algorithm is proposed. The algorithm uses the Transformer Encode module to replace the CSP bottleneck module at the end of the YOLOV5 backbone network， which enhances the network's ability to capture different local information and improves the average accuracy of flame detection. In addition， the CBAM attention module is added to the YOLOV5 networker， which enhances the network's ability to extract image features， and can better extract features for small target flames， reducing the missed detection rate of small target flames. Experiment with the algorithm on the public datasets BoWFire and Bilkent， the experimental results show that the average flame detection accuracy of the improved YOLOV5 network is higher， reaching 83.9%， the small target flame missed detection rate is lower， only 1.6%， and the detection rate is 34 frames/s. Compared with the original YOLOV5 network， the average accuracy is improved 2.4 percentage points， the small target flame missed detection rate is reduced by 4.1 percentage points， the improved YOLOV5 network can meet the real-time and precision requirements of flame detection.

Key words: YOLOV5 algorithm, Transformer, CBAM attention

王洪义, 孔梅梅, 徐荣青. 基于改进YOLOV5的火焰检测算法[J]. 计算机与现代化, 2023, 0(01): 103-107.

WANG Hong-yi, KONG Mei-mei, XU Rong-qing. Flame Detection Algorithm Based on Improved YOLOV5[J]. Computer and Modernization, 2023, 0(01): 103-107.

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