目的 解决现有工业线束导线排序检测方法中存在的效率低、混色导线检测效果差等问题。方法 基于机器视觉技术设计一种线束导线排序检测装置，并结合图像处理技术和深度学习原理提出一种混色导线排序检测方法。首先根据线束图像中选择的感兴趣区域，分割出线束连接器图像和导线图像，并采用模板匹配和颜色定位方法完成连接器正反面的识别和单色导线的识别定位;然后采集并制作PE混色导线数据集，研究Faster R−CNN、SSD、YOLOv3和YOLOv5m等4种不同目标检测算法对PE混色导线的检测效果。结果 实验结果表明，YOLOv5m检测模型的检测速度和准确率兼顾性最好;改进系统后，检测时间减少了18.55%，平均识别准确率为98.83%。结论 改进后检测系统具有良好的检测效率和可靠性，适用于种类丰富的工业线束导线排序检测。

The work aims to solve the problems of low efficiency and poor detection accuracy in the existing sorting and detection methods of mixed color in industrial wires. An wire sorting device based on machine vision technology and an sorting detection approach for mixed-color wires in combination with image processing techniques and deep learning principles are proposed. Firstly, the region of interest in the image was selected manually to segment images of wire connector and wires, and methods of template matching and color localization were applied to realize the identification of the connector's front and back sides and monochrome wires. Secondly, the PE mixed-color wire datasets were produced, and the detection effect of four object detection algorithms, i.e., Faster R-CNN, SSD, YOLOv3, and YOLOv5m was researched. The experimental results indicate that the YOLOv5m model possesses the best capacity between the detection speed and accuracy, which decreases the detection time by 18.55% with an average recognition accuracy of 98.83%, which can be applied to the sorting detection of a variety of industrial wires.