IA-Mask R-CNN: Improved Anchor Design Mask R-CNN for Surface Defect Detection of Automotive Engine Parts

The detection of surface defects on automotive engine parts is an important part of automobile manufacturing quality assurance. The traditional detection methods rely on manual inspection and can be inaccurate and inefficient, while the existing deep learning-based methods, such as the Mask R-CNN detection method, have insufficient precision for detecting minor defects since the anchor scales design does not consider small defects. To overcome these shortcomings, this paper proposes an IA-Mask R-CNN detection method with an improved anchor scales design. First, an image dataset that contains 560 pictures of surface defects of automotive engine parts is established using a 1080P HDMI high-definition digital microscope capable of recording three million real pixels and labeled manually. Then, the anchor scales suitable for the surface defect detection of automotive engine parts are determined by labeled data analysis and used to improve the anchor design in Mask R-CNN. Finally, the proposed method is compared experimentally with the Faster R-CNN and Mask R-CNN, and qualitative and quantitative analyses are conducted. The experimental results show that, without increasing the number of parameters or training time of the Mask R-CNN, the proposed method performed better in detecting minor, as well as larger defects than the other detection methods.

Automated summary

This paper proposes an improved IA-Mask R-CNN detection method for surface defect detection on automotive engine parts. By establishing an image dataset and analyzing labeled data, suitable anchor scales for surface defect detection are determined to improve the anchor design in Mask R-CNN. The experimental results show that the proposed method outperforms other detection methods in detecting both minor and larger defects.