基于改进YOLOv5通用电气控制板装配识别的方法研究

Research on Assembly Identification Method Based on Improved YOLOv5 General Electric Control Board

针对电气控制板在人工装配过程中漏接、漏装及装配准确率低等问题,提出了一种划区域采集装配图像并用改进的YOLOv5模型算法检测电气控制板装配情况的方法。首先,设计了一种双轴活动的相机控制台划区域采集装配情况的图像;其次,添加Squeeze Excitation(SE)模块建立各通道之间的联系,根据不同通道的权重进一步对特征进行加权;再次,将Transformer Prediction Heads(TPH)集成到YOLOv5上,同时用BiFPN替换PANet结构。实验结果表明:基于深度学习改进的YOLOv5算法优于改进前的算法,其器件的检测精度达到96%,检测速率为26帧/秒,能够实现电气控制板上器件的精确检测和识别,满足设备制造过程中对装配准确率提升的需求。

Aiming at the problems of missing connection,missing assembly and lower assembly accuracy in the manual assembly process of electrical control board,an improved YOLOv5 model algorithm is proposed to detect the assembly of electrical control board.Firstly,a two-axis active camera console is designed to collect the image of the assembly area.Then,the Squeeze Excitation(SE)module is added to establish the connection between each channel,and the features are further weighted according to the weight of different channels.Transformer Prediction Heads(TPH)is integrated into YOLOv5,and PANet structure is replaced by BiFPN.The experimental results show that the improved YOLOv5 algorithm based on deep learning is better than that before improvement.The detection accuracy range of the device is 96%and the detection rate is 26 fps.It can realize the accurate detection and recognition of the devices on the electrical control board and meet the needs of improving the assembly accuracy in the equipment manufacturing process.