TY  - JOUR
AU  - FANG, Yiming
AU  - SHI, Zhaoyao
AU  - SONG, Huixu
PY  - 2025
TI  - Detection method for complex dark spots on plastic gears based on U-Net++ and feature fusion
JO  - Journal of Beijing University of Aeronautics and Astronautics
SN  - 1001-5965
SP  - 3020
EP  - 3029
VL  - 51
IS  - 9
AB  - Traditional defect detection algorithms exhibit poor performance in accurately detecting complex dark spots on the surface of plastic gears. There are three primary issues: firstly, inaccurately distinguishing the size and position of dark spots on the gear edge; secondly, a high rate of missed detection for light dark spots; thirdly, a tendency to misjudge the point gate as dark spots. This paper proposed an improved detection method for complex dark spots on plastic gears based on U-Net++ and feature fusion. The dark spot area was predicted through U-Net++ and corrected depending on gradient features. Multi-feature fusion analysis was utilized to provide the final judgment result, thus improving the accuracy and stability of complex dark spot detection. The test results demonstrate that the Pc value, which represents the accuracy of the detection results, is as high as 98.93%, and the average value of IoU, representing the accuracy of the segmentation results, reaches 0.864. In comparison to traditional defect detection algorithms and uncorrected deep learning algorithms, the proposed method increases the average value of IoU by 0.478 and 0.309, respectively.
UR  - https://doi.org/10.13700/j.bh.1001-5965.2023.0418
DO  - 10.13700/j.bh.1001-5965.2023.0418