Next Article in Journal
Analysis of a Low-Cost EEG Monitoring System and Dry Electrodes toward Clinical Use in the Neonatal ICU
Next Article in Special Issue
A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving
Previous Article in Journal
User-Oriented ICT Cloud Architecture for High-Accuracy GNSS-Based Services
Previous Article in Special Issue
Collaborative Representation Using Non-Negative Samples for Image Classification
Open AccessArticle

An Industrial Micro-Defect Diagnosis System via Intelligent Segmentation Region

School of Manufacturing Science and Engineering, Sichuan University, Chengdu 610065, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2636; https://doi.org/10.3390/s19112636
Received: 8 May 2019 / Revised: 5 June 2019 / Accepted: 6 June 2019 / Published: 11 June 2019
In the field of machine vision defect detection for a micro workpiece, it is very important to make the neural network realize the integrity of the mask in analyte segmentation regions. In the process of the recognition of small workpieces, fatal defects are always contained in borderline areas that are difficult to demarcate. The non-maximum suppression (NMS) of intersection over union (IOU) will lose crucial texture information especially in the clutter and occlusion detection areas. In this paper, simple linear iterative clustering (SLIC) is used to augment the mask as well as calibrate the score of the mask. We propose an SLIC head of object instance segmentation in proposal regions (Mask R-CNN) containing a network block to learn the quality of the predict masks. It is found that parallel K-means in the limited region mechanism in the SLIC head improved the confidence of the mask score, in the context of our workpiece. A continuous fine-tune mechanism was utilized to continuously improve the model robustness in a large-scale production line. We established a detection system, which included an optical fiber locator, telecentric lens system, matrix stereoscopic light, a rotating platform, and a neural network with an SLIC head. The accuracy of defect detection is effectively improved for micro workpieces with clutter and borderline areas. View Full-Text
Keywords: non maxima suppression; intersection over union; simple linear iterative clustering; segmentation quality; predicted masks; parallel K-means; continuous fine-tune non maxima suppression; intersection over union; simple linear iterative clustering; segmentation quality; predicted masks; parallel K-means; continuous fine-tune
Show Figures

Figure 1

MDPI and ACS Style

Fang, X.; Jie, W.; Feng, T. An Industrial Micro-Defect Diagnosis System via Intelligent Segmentation Region. Sensors 2019, 19, 2636.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop