首页|基于改进YOLOv7的换热器板片故障检测算法研究

基于改进YOLOv7的换热器板片故障检测算法研究

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针对换热器板片装配检测成本高、检测精度低等问题,提出一种改进的YOLOv7算法检测换热器板片的装反问题.在原始数据集上采用限制对比度自适应直方图均衡化对图像进行处理,提高待检测图像的清晰度;在YOLOv7模型原有基础上,针对换热器板片层数较多、模糊且噪点多,提出一种多级自适应注意力机制添加至Backbone最后一层;针对图像识别模型计算量大、下采样特征损失严重,采用改进的MP-D模块优化原有的下采样模块;针对特征提取部分,加入F-ReLU激活函数,使得计算速度和检测准确性有了明显提高;针对Neck部分的PANet结构,融合BiFPN跨尺度连接的思想,进一步提高融合的效率和准确性.通过实验可得,改进后的网络模型和初始YOLOv7相比,mAP@0.5、召回率R、检测速度分别提高0.6%、2%、16.9帧/s,针对换热器板片检测具有良好效果.
Research on Fault Detection Algorithm of Heat Exchanger Plate Based on Improved YOLOv7
Aiming at the high cost and low detection accuracy of heat exchanger plate assembly inspection,an improved YOLOv7 algorithm was proposed to detect the reverse installation of heat exchanger plate.On the original data set,the limited contrast adaptive histogram equalization was used to process the image to improve the clarity of the image to be detected;on the basis of the original YOLOv7 model,for the layers number of the heat exchanger plate is large,blurred and much noisy,a multi-level adaptive attention mechanism was proposed to add to the last layer of Backbone;in view of the large amount of calculation of the image recognition model and the serious loss of downsampling features,the improved MP-D module was used to optimize the original downsampling module;in the feature extraction part,the F-ReLU activation function was added to significantly improve the calculation speed and detection accu-racy;for the PANet structure of the Neck part,the idea of cross-scale connection of BiFPN was integrated to further improve the effi-ciency and accuracy of fusion.Through experiments,it can be seen that compared with the original YOLOv7,the improved network model has mAP@0.5,recall rate R,and inspection speed increased by 0.6%,2%,and 16.9 frame per second,respectively.It has a good effect on the detection of heat exchanger plates.

heat exchanger plateYOLOv7multi-level adaptive attention mechanismMP-D moduleBiFPN

王伯涛、周福强、吴国新、王少红

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北京信息科技大学,现代测控技术教育部重点实验室,北京 100192

换热器板片 YOLOv7 多级自适应注意力机制 MP-D模块 BiFPN

北京信息科技大学勤信人才项目

QXTCPC202120

2024

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子:0.32
ISSN:1001-3881
年,卷(期):2024.52(11)
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