A cascading fuzzy logic with image processing algorithm–based defect detection for automatic visual inspection of industrial cylindrical object’s surface

Most of the research studies nowadays are trying to bring automation to biomedical engineering and Lab on a Chip which is fast growing interdisciplinary field and has attracted researchers from various fields. The objective of this paper is to present an overall system to control droplet movement inside microfluidic channel using fuzzy logic controller, image processing algorithm, and microvalves installed within microfluidic channel. A state space model has been derived from circuit analogy approach to describe the microfluidic network. Furthermore, a COMSOL-based study is primed for device structure by means of droplet generation and controlling the droplet through fitted valves. Moreover, an image processing algorithm based on active contours has been proposed in this research to track the movement of the droplet through the channel. This droplet controlling method is utterly based on fuzzy controller as well as camera images to move the droplet at desired position by controlling flow rates inside the fluidic channel using valves installed inside the microfluidic device. The results indicate that the fuzzy logic controller performs much better in terms of stability and faster response as compared to conventional proportional–integral–derivative controller.

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Design of Automated Visual Inspection System for Beverage Industry Production Line

Traitement du signal ◽

10.18280/ts.380225 ◽

2021 ◽

Vol 38 (2) ◽

pp. 461-466

Author(s):

Subhransu Padhee ◽

Durgesh Nandan

Keyword(s):

Image Processing ◽

Production Line ◽

Visual Inspection ◽

Conveyor Belt ◽

Processing Algorithm ◽

Inspection System ◽

Automated Visual Inspection ◽

Image Processing Algorithm ◽

Overall Design ◽

Beverage Industry

This paper provides an overall design and implementation perspective of a laboratory-scale automated visual inspection system for the beverage industry's production line. A case study has been undertaken where the image processing algorithm inspects the beverage bottle for any defects. Different defects such as improper labeling and improper liquid level can be detected using the image processing algorithm. A laboratory prototype of the conveyor belt has been built, and a prototype filling plant has been established to verify the simulation results.

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Online defect detection method of array optical sensor based on watershed image processing algorithm

Multimedia Tools and Applications ◽

10.1007/s11042-019-7340-y ◽

2019 ◽

Author(s):

Xuanni Zhang

Keyword(s):

Image Processing ◽

Defect Detection ◽

Optical Sensor ◽

Detection Method ◽

Processing Algorithm ◽

Image Processing Algorithm

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Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i4.pp3093-3105 ◽

2021 ◽

Vol 11 (4) ◽

pp. 3093

Author(s):

Victor Fernandez ◽

Javier Chavez ◽

Guillermo Kemper

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Visual Inspection ◽

Fiber Optic ◽

Electronic Device ◽

Digital Camera ◽

Optical Microscope ◽

Processing Algorithm ◽

Image Processing Algorithm

This work proposes a portable, handheld electronic device, which measures the cleanliness in fiber optic connectors via digital image processing and artificial neural networks. Its purpose is to reduce the evaluation subjectivity in visual inspection done by human experts. Although devices with this purpose already exist, they tend to be cost-prohibitive and do not take advantage of neither image processing nor artificial intelligence to improve their results. The device consists of an optical microscope for fiber optic connector analysis, a digital camera adapter, a reduced-board computer, an image processing algorithm, a neural network algorithm and an LCD screen for equipment operation and results visualization. The image processing algorithm applies grayscale histogram equalization, Gaussian filtering, Canny filtering, Hough transform, region of interest segmentation and obtaining radiometric descriptors as inputs to the neural network. Validation consisted of comparing the results by the proposed device with those obtained by agreeing human experts via visual inspection. Results yield an average Cohen's Kappa of 0.926, which implies a very satisfactory performance by the proposed device.

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