Micro-oxidation is a fatal problem for some precision oxygen-free copper materials, and it is difficult to detect with the naked eyes. However, manual inspection using microscope equipment is expensive, subjective, and time-consuming. The automatic high-definition micrograph system equipped with micro-oxidation detection algorithm can detect more quickly, efficiently, and accurately. In this study, a micro-oxidation small objection detection model, MO-SOD, is proposed to detect the oxidation degree on oxygen-free copper surface based on microimaging system. This model is developed for rapid detection on the robot platform combined with high-definition microphotography system. The proposed MO-SOD model consists of three modules: small target feature extraction layer, key small object attention pyramid integration layer, and anchor-free decoupling detector. The small object feature extraction layer focuses on the local features of small object to improve the perception of micro-oxidation spots and also takes the global features into account to reduce the impact of noisy background on feature extraction. Key small object attention pyramid integration block couples key small object feature attention and pyramid to detect the micro-oxidation spots in the image. The performance of MO-SOD model is further improved by combining the anchor-free decoupling detector. In addition, the loss function is improved to combine CIOU loss and focal loss to achieve effective micro-oxidation detection. The MO-SOD model is trained and tested from three oxidation levels in an oxygen-free copper surface microscope image data set. The test results show that the average accuracy (mAP) of MO-SOD model is 82.96%, which is superior to other most advanced detectors.
© 2023 The Authors. Published by American Chemical Society.