关键词: 水电厂, YOLOv5, 算法模型, 工器具识别

Abstract: With the introduction of the concept of smart power plants, the traditional management mode of hydropower plants is gradually transitioning towards a smart management system. Based on the current management status of maintenance tools in hydropower plants, and combined with the rapid development of deep convolutional neural network technology in the field of image recognition in recent years, especially with the increasingly mature object detection technology, a tool recognition system based on YOLOv5 neural network model is proposed, aiming to achieve accurate recognition of maintenance tools in hydropower plants. The system utilizes calibration tool data. And the collected data is divided into training set, validation set, and testing set, which are used for neural network training, model validation, and performance evaluation, respectively. The training results show that the system displays excellent recognition ability with precision (P), recall (R), and mean average values (mAP) of 90.3%, 72%, and 83.4%, increased by 2.1%, 1.6% and 1.3%, respectively compared to the original model. Further testing is conducted on the trained network using the validation set, and the test results show that the system can accurately identify the types of tools, automatically generating visualized anchor frames during the recognition process. And it accurately displays the coordinates and confidence of anchor frames. This feature not only improves the efficiency of access and retrieval work, but also enhances the accuracy and convenience of tool management. The system has demonstrated high recognition accuracy and stability, providing strong technical support for the intelligent management of hydropower plants.

Key words: hydroelectric plant, Yolov5, algorithm model, tool recognition