Defect detection of grinded and polished workpieces using faster R-CNN
Journal
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Journal Volume
2021-July
Pages
1290-1296
Date Issued
2021
Author(s)
Abstract
Polishing and grinding are crucial in the fabrication processes of industrial and commercial products. While the fabrication process can be automated using robots or specialized machines, experienced workers are still needed for the subsequent quality inspection. Here, we report the development of an automatic defect detection system, which is capable of detecting the defects of grinded and polished faucets due to its Faster Region-based Convolutional Neural Networks (Faster RCNN) architecture. The images of the workpieces were taken using a manipulator with a preset trajectory to cover all the surfaces of the workpieces. After labeling, the data were augmented to the trainable level. Three pretrained CNN-based models were utilized and evaluated. The hyperparameters were analyzed to validate their effect on the performance of the model. The mean average precision, using the tuned hyperparameters, was 80.26%. ? 2021 IEEE.
Subjects
Defect detection
Faster RCNN
Grinding
Image augmentation
Manipulator
Polishing
Defects
Intelligent mechatronics
Automatic defect detections
Commercial products
Fabrication process
Hyperparameters
Preset trajectory
Quality inspection
Region-based
Convolutional neural networks
Type
conference paper