To address the problem that the direction of pipe cracks is difficult to detect, a crack direction recognition method based on prototype learning is proposed with a prototype network as a framework. First, through the convolutional layer of the prototype network, shallow features of crack directions are extracted to improve the generalization ability of the model on the data set of this paper. then, by improving the High-Resolution Network and introducing a location self-attention mechanism, and combined with a migration training method for the data set of this paper, a category that can accurately reflect the crack directions is constructed prototype learning mechanism. Finally, pattern recognition is performed by the metric classification methods, the effective classification of crack direction under small sample condition is achieved. The experimental results show that the recognition accuracy of the crack direction recognition method based on prototype learning can reach 99.2% with the sample parameters unchanged.
Aiming at the problems that hidden defects inside objects are difficult to be visually recognized and the defect samples obtained from inspection are few, a small-sample learning detection model using ultrasonic flaw detection to extend machine vision is proposed. The model introduces an attention mechanism into the deep nearest neighbor network to adjust the image features, so that the model pays more attention to the useful defect area features, increases the amount of defect-related information, and makes full use of key defect features to detect image targets. Experiments show that the proposed method has the best performance compared with the baseline model on the self-made hidden defect dataset, and the average correct rate is up to 83.85% under 10-shot; the model is tested with noisy images, and the results show that the model detection under noisy conditions has an accuracy rate of about 76%, and it has a certain anti-noise interference ability.
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