Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Yang, Jing; Li, Shaobo; Wang, Zheng; Yang, Guanci

doi:10.1109/access.2019.2925561

Cited by 107 publications

(54 citation statements)

References 40 publications

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“…The comparison between Figs. 11 and 12 reveals that the contrast between the normal region and the defective region is improved by using (6) and (7). According to (8)- (14), the detection results for dispersed defects are given in Fig.…”

Section: Experimental Studymentioning

confidence: 95%

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Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

2020

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This paper is concerned with detection of dispersed defects in the resin eyeglass. At present, manual detection is always used in industry, which inevitably causes impairing eyes and a high rate of false-negative detection. Statistics show that its average accuracy and the average detection time are about 85% and 10s, respectively. We for the first time propose an automatic approach to detection of dispersed defects in resin eyeglass, based on the machine vision technology. It is observed that the refractivity of the normal and the defective regions of an eyeglass are different, and thus the reflection image is also collected in our system in addition to the normal transmission image. Such an optical imaging system is modelled and its analysis shows the gray-scale gradient difference between the normal and defective regions in the acquired image is dramatically enhanced under the illumination of a point light source with our designed system. An image processing algorithm is then developed to reveal the above difference and detect dispersed defects in resin eyeglasses. Our simulation study verifies the proposed approach. Further, its experimental evaluation was carried out and the result was consistent with the simulation one, showing that its detection accuracy and the average detection time were 97.50% and 0.636s, respectively, which meet the requirements for online detection of dispersed defects. INDEX TERMS Automatic detection, dispersed defects, imaging analysis, machine vision, resin eyeglass.

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Section: Experimental Studymentioning

confidence: 95%

“…Machine vision is an effective technology to support online defect detection [4]- [6], and the existing relevant work focuses mainly on detection of defects on optical components made of glass and resin. Various technologies have been developed to detect defects on optical components made of glass.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

2020

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“…Online Platform for Industrial Defect Detection Figure 4 depicts the system flow chart of the online testing platform for gear manufacturing defects designed by the research group. Figure 5 is an online test platform for gear manufacturing defects built by the research team [10,32]. This platform includes the conveyor belt, data processor, data acquisition sensor, light source, and other mechanical supports, wherein the touch display for inputting and displaying data is the 32-inch industrial touch screen.…”

Section: K-meansmentioning

confidence: 99%

Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network

Wang

Duan

2019

Journal of Sensors

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A novel, efficient, and accurate method to detect gear defects under a complex background during industrial gear production is proposed in this study. Firstly, we first analyzed image filtering and smoothing techniques, which we used as a basis to develop a complex background-weakening algorithm for detecting the microdefects of gears. Subsequently, we discussed the types and characteristics of gear manufacturing defects. Under the complex background of image acquisition, a new model S-YOLO is proposed for online detection of gear defects, and it was validated on our experimental platform for online gear defect detection under a complex background. Results show that S-YOLO has better recognition of microdefects under a complex background than the YOLOv3 target recognition network. The proposed algorithm has good robustness as well. Code and data have been made available.

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“…Object detection use cases range from support for autonomous driving [1] over medical diagnostics [2] to product quality control [3]. Existing object detection models can also be used for labeling and annotating images in various scenarios.…”

Section: Introductionmentioning

confidence: 99%

Deep learning object detection as an assistance system for complex image labeling tasks

Leimkühler

Gravemeier

Biester³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Object detection via deep learning has many promising areas of application. However, robustness and accuracy of fully automated systems are often insufficient for practical use. Integrating results from Artificial Intelligence (AI) and human intelligence in collaborative settings might bridge the gap between efficiency and accuracy. This study proves increased efficiency when supporting human intelligence through AI without negative impact on effectiveness in a finegrained car scratch image labeling task. Based on the confirmed benefits of AI with human intelligence in the loop approaches, this contribution discusses potential practical application scenarios and envisions the implementation of assistance systems supported by computer vision.

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Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Cited by 107 publications

References 40 publications

Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

Automatic Detection of Dispersed Defects in Resin Eyeglass Based on Machine Vision Technology

Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network

Deep learning object detection as an assistance system for complex image labeling tasks

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