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.