A Real-Time Apple Grading System Using Multicolor Space

This study was focused on the multicolor space which provides a better specification of the color and size of the apple in an image. In the study, a real-time machine vision system classifying apples into four categories with respect to color and size was designed. In the analysis, different color spaces were used. As a result, 97% identification success for the red fields of the apple was obtained depending on the values of the parameter “a” of CIEL*a*b*color space. Similarly, 94% identification success for the yellow fields was obtained depending on the values of the parameteryof CIEXYZcolor space. With the designed system, three kinds of apples (Golden, Starking, and Jonagold) were investigated by classifying them into four groups with respect to two parameters, color and size. Finally, 99% success rate was achieved in the analyses conducted for 595 apples.

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A real-time machine-vision system for monitoring the textile raising process

Computers in Industry ◽

10.1016/j.compind.2005.05.010 ◽

2005 ◽

Vol 56 (8-9) ◽

pp. 831-842 ◽

Cited By ~ 22

Author(s):

Monica Carfagni ◽

Rocco Furferi ◽

Lapo Governi

Keyword(s):

Machine Vision ◽

Real Time ◽

Vision System ◽

Machine Vision System ◽

Time Machine

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A real-time machine vision system for visual information processing

Proceedings of IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.1994.400037 ◽

2002 ◽

Cited By ~ 3

Author(s):

L. Guan ◽

H. Kong

Keyword(s):

Information Processing ◽

Machine Vision ◽

Real Time ◽

Visual Information ◽

Vision System ◽

Visual Information Processing ◽

Machine Vision System ◽

Time Machine

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Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model

Textile Research Journal ◽

10.1177/00405175211034241 ◽

2021 ◽

pp. 004051752110342

Author(s):

Sifundvolesihle Dlamini ◽

Chih-Yuan Kao ◽

Shun-Lian Su ◽

Chung-Feng Jeffrey Kuo

Keyword(s):

Machine Vision ◽

Real Time ◽

Defect Detection ◽

Data Augmentation ◽

Detection System ◽

Vision System ◽

Good Precision ◽

Machine Vision System ◽

Time Machine ◽

Textile Fabric

We introduce a real-time machine vision system we developed with the aim of detecting defects in functional textile fabrics with good precision at relatively fast detection speeds to assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of [Formula: see text], and recall and [Formula: see text] scores of [Formula: see text] and [Formula: see text], respectively. The detection speed is relatively fast at [Formula: see text] fps with a prediction speed of [Formula: see text] ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.

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