关键词: 深度学习, 缺陷检测, 铝合金压铸件, 微小缺陷

Abstract: Aiming at the problems of tiny defects in complex castings,complex background interference,and difficulty in high-precision defect detection,a casting defect detection model named single-shot Refinement neural network for Aluminum Casting Tiny Defects Detection (Refine-ACTDD) based on deep learning was proposed.Based on the single-shot Refinement neural network for Object Detection (RefineDet) algorithm,a close-packed anchor design method was adopted to improve the accuracy of small defects,and an attention mechanism was introduced to reduce the interference of complex background detection.At the same time,a method that combined deep learning and contour discovery was proposed to realize end-to-end detection of small sample defects.After data collection,a large data set ALU-DEF containing 7816 images of appearance defects of castings was produced.The comparative training method was used on the data set to train and test.Experimental results showed that the algorithm could achieve an average accuracy of 95.44%,which had a higher Mean of Average Precision (MAP) and accuracy than faster-RCNN and YOLOv3.

Key words: deep learning, defect detection, aluminum alloy die castings, tiny defects