| Title |
Mask R-CNN-based Occlusion Anomaly Detection Considering Orientation in Manufacturing Process Data |
| Authors |
(Seo-El Lee) ; (So-Eun Choi) ; (Geon Park) ; (Ye-Yeon Kang) ; (Ji-Won Baek) ; (Kyungyong Chung) |
| DOI |
https://doi.org/10.5573/IEIESPC.2022.11.6.393 |
| Keywords |
Manufacturing process; Orientation; Object detection; Mask R-CNN; Anomaly detection |
| Abstract |
In a manufacturing process, data analysis is conducted to identify defective products in real time to lower their massive production and improve the rate of efficient production. In the production process, it is difficult to find defective products mixed with normal products. Therefore, it is necessary to detect defective products generated in the production process and reduce the risk of their production. Consequently, this study proposes the Mask R-CNN-based occlusion anomaly detection method in consideration of the orientation of manufacturing process data. The proposed method uses Mask R-CNN to find abnormal objects, such as occluded objects, in a manufacturing process line. In the manufacturing process, some products are hidden. Accordingly, preprocessing in consideration of multiple orientations is applied to generate data. The generated data is performed to detect occlusions and anomalies using Mask R-CNN. The mean of IoU was compared to evaluate the detection accuracy of YOLO and Mask R-CNN. YOLO showed excellent performance when there was a constant distance and orientation and no occluded object. However, Mask R-CNN performed excellently when there was any occluded object and the orientation was considered. Therefore, for occlusion anomaly detection in a manufacturing process, Mask R-CNN can reduce the production rate of defective products. |