Convolutional neural network-based wind driven generator blade crack detection method

Abstract

The invention belongs to the technical field of computer vision, and discloses a convolutional neural network-based wind driven generator blade crack detection method, which comprises the following steps of (1) acquiring a generator blade image, and constructing a learning model training sample; (2) training a convolutional neural network model by using the model training sample; (3) preprocessingthe to-be-detected image; (4) carrying out feature extraction on the image by adopting a feature extraction network to obtain a feature map; (5) inputting the feature map into an area generation network to obtain a blade existence probability in each candidate box and the candidate box initial position coordinates; (6) performing the threshold filtering and non-maximum suppression on the candidate box; (7) inputting the feature map of each candidate box region into an interested region pooling layer and a frame regression network to obtain the candidate box correction coordinates; and (8) inputting the original image areas corresponding to the candidate frames into a classification network, and judging a blade crack classification result. According to the method, the interference of the image background content is eliminated, and the blade detection precision is improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for detecting cracks in blades of wind power generators based on a convolutional neural network (CNN, Convolutional Neural Network). Background technique [0002] As a mature green energy technology, wind power has the advantages of low cost, clean use, flexible installed capacity, and renewable. Vigorously developing wind power generation is of great significance for reducing fossil energy consumption, reducing greenhouse gas emissions, and alleviating energy shortages. my country's wind energy reserves are large, widely distributed, and have great potential for development and utilization. With the proposal and implementation of the new energy development strategy, my country's wind power industry has entered a stage of leapfrog development. [0003] Due to geographical reasons, most of my country's wind power plants are distributed in areas with ...

AI Technical Summary

Problems solved by technology

Among them, the detection workload of the potential method and the direct microscope observation method is huge and the efficiency is low. These methods consume a lot of human resources for the detection of leaves and are not suitable for mass detection.

Acoustic emission technology and ultrasonic detection technology are suitable for real-time dynamic monitoring and detection. They have the advantages of providing overall and large-scale rapid detection, and are not sensitive to the geometric shape of the detection structure. However, these technologies need to attach sensors to the generator blades, and at the same time The equipment used in these methods is expensive and the cost is high

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