An aluminum material surface defect detection algorithm based on deep learning

Abstract

The invention relates to an aluminum material surface defect detection algorithm based on deep learning, and the algorithm comprises the steps: (1) employing a camera to shoot the surface of an aluminum material, obtaining a related data set, employing a labelImg tool to label an image, and obtaining label information; (2) dividing the image into a training set and a test set, and performing dataenhancement on the training set; (3) inputting a defective image, a non-defective image and label information of the defective image into the network at the same time every time to carry out model training; and (4) inputting the test image into the trained aluminum material surface defect detection model, and obtaining the position and the corresponding category of the defect. According to the method, a defective image and a non-defective image can be effectively utilized; the generalization ability and the detection precision of the model are improved, the detection performance is further improved by fully utilizing context information around the candidate region, the detection performance of dense small defects can be improved by utilizing a soft non-maximum suppression algorithm, and the method is an efficient aluminum material surface defect detection algorithm.

Description

technical field [0001] The invention relates to the field of image target detection, namely a deep learning-based aluminum surface defect detection algorithm. Background technique [0002] In the actual production process of aluminum materials, due to the influence of various factors, the surface of aluminum materials will have defects such as dirty spots, non-conductivity and bottom leakage, which will seriously affect the quality of aluminum materials. In order to ensure product quality, manual visual inspection is required. However, the surface of aluminum itself contains lines, which are not highly distinguishable from defects. Traditional manual inspection with naked eyes is very laborious and cannot accurately determine surface defects in a timely manner, making it difficult to control the efficiency of quality inspection. [0003] With the development of deep learning, especially the application of convolutional neural network in image recognition, image detection an...

AI Technical Summary

Problems solved by technology

[0004]However, the detection performance of general-purpose target detection algorithms for aluminum surface defects is not nearly satisfactory. The main reason is that the number of aluminum defect images is small, and the size of aluminum surface defects Large changes, not fixed shape, etc.

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