A Review on Face Detection Techniques

Hire, Ms. A. N.; M.P.Satone, Prof. Dr.

doi:10.31142/ijtsrd14107

Cited by 1 publication

(1 citation statement)

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“…It assists the industry in increasing production efficiency through automatic quality control to inspect the products in manufacturing sectors, online monitoring systems, reducing human efforts and reducing safety risks [3]. The major application fields of machine vision are defects checkup in objects/jobs [4], edge detection on the fabricated components, barcodes and text interpretation to reveal the products, which is far faster and more precise than manual error-proofing method, assembly and integration, Personal Protective Equipment (PPE) recognition, predictive care, construction management, crop planting and harvest monitoring, automatic weeding systems, facial recognition technology, medical image analysis, self-VOLUME 34 (YEAR XXXIV) 2023 driving cars, intelligent video analytics, Pharmaceutical Drugs [5], and insects finding. Edge detection and geometrical analysis of the object is a kind of image processing technique.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection and Defects Checking of Binder Clip and Welded Joint using a Python-Based Algorithm: Applications in Quality Inspection

Senthil Murugan,

Sathiya,

Hariharan

et al. 2023

AWET

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Machine vision is a computer vision system that enables a computer to work on image-based inspection and analysis for different applications. In this computer vision, a camera and sensor were used to view an image for its analysis with the help of some sort of algorithms, then processed to infer the image-based data. Machine vision systems along with Python programs can be used for many interdisciplinary applications like weld inspection, online monitoring in manufacturing auto components etc. In this study, the “Edge detection python algorithm” was developed and run through “Google Colab” notebook to inspect the edges and the boundaries of samples like faying surface-modified friction welded dissimilar joints and a binder clip (paper clamp) to check any defects or cracks and straightness etc. With the help of this Python algorithm, the edge detection was done by Sobel, Scharr, and Prewit operators. An input image of the weld joint and the binder clip were converted into Otsu’s binary threshold image. The matrix vision camera and the CMOS sensor were used in the machine vision set-up to take the images. This written algorithm is helpful to trace the edges of any kind of solids components. The edges of the binder clips and the weld joint/zone were detected. The binder clips were inspected under two different cases namely the clip in folding condition (Case I) and the binder clip in unfolding condition (Case II). The results showed a defect that was identified in the weld zone and no bending was in the binder clips. This kind of study is useful in manufacturing industries for quality inspection purposes with a new machine vision set up for online inspection of fabricated components like nuts and bolts etc.

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Section: Introductionmentioning

confidence: 99%