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.