In the field of vacuum leak detection in fusion devices, the future fusion devices are operated with tritium and the leak checkers do not have access to the devices for leak checking, which makes this task extremely difficult and time-consuming. In order to realize the fast and accurate detection of fusion device leakage equipment,and realize the fast and accurate detection of fusion device leakage equipment, this paper takes the six-degree-of-freedom robotic arm as the research object, and proposes a GV2-YOLOv5 vacuum equipment detection method for vacuum leakage detection robots to identify and locate the vacuum equipment for helium injection. In this method, the C3GhostV2 module is constructed by combining lightweight GhostNetV2 network, while using lightweight GhostConv to extract target features, thus reducing the number of model parameters and improving the computational speed. Bottleneck Transformers and ECA Attention mechanism are added to the feature fusion network to improve the network feature extraction capability and to enhance the model channel features. The experimental results show that the average accuracy of the improved model is 93. 2% on the homemade dataset, which is 1. 4% higher than YOLOv5s, the amount of model parameters is reduced by 29. 5%, and the detection speed is 92 fps, which meets the requirements of real-time and accuracy, and provides a solution for the vision localization technology of vacuum leak detection robot.