The invention discloses a real-time sleeper defect detection method based on a pruning algorithm. The method specifically comprises the steps that a high-speed camera obtains a sleeper image; image data are enhanced and expanded, and a training set is made according to the number of normal sleepers, sleeper falling blocks and sleeper cracks being 1: 1: 1; marking the training set; carrying out model basic training on the training set by adopting a deep learning YOLOv3 target detection algorithm; carrying out sparse training on the model, and judging the importance of each channel and layer in the sleeper defect detection network by utilizing a gamma coefficient of a BN layer; importance sorting is carried out, and a pruning proportion is set; pruning unimportant channels and layers of the network; and finely adjusting the pruned model. On the system level, the sleeper defect detection speed is effectively increased, and deployment in embedded equipment and online detection can be achieved; in the model level, the importance of each channel of the network is judged through the gamma coefficient, and the method has the advantages of simple principle and simplicity and convenience in operation.
本发明公开了一种基于剪枝算法的轨枕实时缺陷检测方法,具体为:高速相机获取轨枕图像;对图像数据增强并扩充,按照正常轨枕、轨枕掉块、轨枕裂缝数量为1:1:1制作训练集;对训练集进行标注;采用深度学习YOLOv3目标检测算法对训练集进行模型基础训练;对模型稀疏训练,利用BN层的γ系数评判轨枕缺陷检测网络中各通道和层的重要性;进行重要性排序,并设定剪枝比例;对网络不重要的通道和层进行剪枝;将剪枝完成的模型进行微调。本发明在系统层面,有效加快了轨枕缺陷检测速度,可实现在嵌入式设备部署并在线检测;在模型层面,通过γ系数大小评判网络各通道重要性,具有原理简单、操作简便的优点。
Real-time sleeper defect detection method based on pruning algorithm
一种基于剪枝算法的轨枕实时缺陷检测方法
2022-08-02
Patent
Electronic Resource
Chinese
IPC: | G06T Bilddatenverarbeitung oder Bilddatenerzeugung allgemein , IMAGE DATA PROCESSING OR GENERATION, IN GENERAL / B61K Andere Hilfseinrichtungen für Eisenbahnen , OTHER AUXILIARY EQUIPMENT FOR RAILWAYS / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V |
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