The invention belongs to the field of track foreign matter detection, and discloses a track foreign matter detection method based on a YOLO algorithm. Comprising the following steps: collecting track foreign matter images to form a data set, and performing sample expansion processing and label processing on each image in the data set so as to obtain a sample set; a channel attention mechanism and a direction perception mechanism are added into the YOLOv5, constraint conditions are set, and then an improved YOLOv5 algorithm is obtained and marked as a G-YOLOv5 model; a G-YOLOv5 model is pre-trained by using the sample set, and a trained G-YOLOv5 model is obtained; a real-time image of the track is collected and processed, the processed image is imported into the trained G-YOLOv5 model, and a real-time detection report is obtained; the obtained real-time detection report is arranged on a visual interface, and efficient track foreign matter detection based on the YOLO algorithm is achieved.
本发明属于轨道异物检测领域,本发明公开了一种基于YOLO算法的轨道异物检测方法;包括:收集轨道异物图像形成数据集,对数据集中的每个图像进行样本扩充处理和标签处理,进而获取样本集;在YOLOv5中加入通道注意力机制和方向感知机制,并设置约束条件,进而获取改进后的YOLOv5算法,记为G‑YOLOv5模型;使用样本集对G‑YOLOv5模型进行预训练,获取训练好的G‑YOLOv5模型;采集轨道的实时图像并进行处理,将处理后的图像导入训练好的G‑YOLOv5模型,获取实时的检测报告;将获取实时的检测报告布置在可视化界面上,实现基于YOLO算法的轨道异物高效检测。
Track foreign matter detection method based on YOLO algorithm
一种基于YOLO算法的轨道异物检测方法
2025-02-18
Patent
Electronic Resource
Chinese
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