Role of Computer Vision in Automatic Inspection Systems

Neethu, N.; Anoop, B. K.

doi:10.5120/ijca2015905603

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…The utilization of computer vision is carried out because of its efficiency and effectiveness. Hence, computer vision is often applied for detection, classification, and implementation due to its efficiency and effectiveness when utilized in common problems [2].…”

Section: Introductionmentioning

confidence: 99%

Text Recognition for Library Collection in Different Light Conditions

Yuadi,

Sigh,

Nihaya

2024

TEM Journal

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Book arrangement problems in Indonesia mainly utilize traditional systems. However, it contributes to user frustration and dissatisfaction, leading to difficulties in the retrieval process and hindering book monitoring. Therefore, an efficient arrangement is needed to make it easier for librarians to organize collections at the library. Hence, this study proposes and evaluates Optical Character Recognition (OCR) approaches such as Tesseract-OCR and Efficient and Accurate Scene Text (EAST) by considering various light factors and device applications. An evaluation was performed to determine the effectiveness of the methods for detecting classification numbers using the Character Error Rate (CER) and Word Error Rate (WER). The results obtained can be seen from the lighting and the use of image capture tools that can affect the text detection results. The higher the lighting, the better the text detection results obtained. However, in the extraction tool using a security camera yields a better output than the others. In conclusion, the modernization of book arrangement systems by implementing OCR through security cameras could enhance user experience in the retrieval process.

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Section: Introductionmentioning

confidence: 99%

Text Recognition for Library Collection in Different Light Conditions

Yuadi,

Sigh,

Nihaya

2024

TEM Journal

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“…The utilization of computer vision has witnessed substantial growth in recent years, leading to the development of advanced applications and automation systems capable of real-time object detection, distinction, and localization in images and videos [4,6,7]. Detection, unlike classification, is a more intricate process.…”

Section: Introductionmentioning

confidence: 99%

Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions

Kurniawan,

Astawa,

Atmaja

et al. 2023

regist. j. ilm. teknol. sist. inf.

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The Covid-19 pandemic has made it imperative to prioritize health standards in companies and public areas with a large number of people. Typically, officers oversee the usage of masks in public spaces; however, computer vision can be employed to facilitate this process. This study focuses on the detection of facemask usage utilizing the YOLO-v5 algorithm across various datasets and resolutions. Three datasets were employed: the face with mask dataset (M dataset), the synthetic dataset (S dataset), and the combined dataset (G dataset), with image resolutions of 320 pixels and 640 pixels, respectively. The objective of this study is to assess the accuracy of the YOLO-v5 algorithm in detecting whether an individual is wearing a mask or not. In addition, the algorithm was tested on a dataset comprising individuals wearing masks and a synthetic dataset. The training results indicate that higher resolutions lead to longer training times, but yield excellent prediction outcomes. The system test results demonstrate that face image detection using the YOLO-v5 method performs exceptionally well at a resolution of 640 pixels, achieving a detection rate of 99.2 percent for the G dataset, 98.5 percent for the S dataset, and 98.9 percent for the M dataset. These test results provide evidence that the YOLO-v5 algorithm is highly recommended for accurate detection of facemask usage.

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“…Traditional methods of detecting material defects, such as manual inspections or simple visual algorithms, can be unreliable and, above all, time-consuming. In recent years, however, new tools based on intelligent algorithms (Liu et al, 2023) or structures (Neethu et al, 2015), such as neural networks, have emerged that enable automation and significantly increase the precision of defect detection. One of the most popular tools for object detection is the YOLOv5 (Redmon et al, 2016) (You Only Look Once version 5) model, which is based on neural networks and designed for fast and accurate object detection in images.…”

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confidence: 99%

An Application of a Material Defect Detection System Using Artificial Intelligence

Wróbel,

Szymczyk

2023

IJMMT

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In recent years, detecting defects in materials has become an important element in many industries, such as automotive, construction, textile manufacturing, and many others. Material defects, such as stains, cracks, dents, and others, can affect the quality and durability of the product, which can have serious consequences for safety, performance, and product quality. Therefore, it is important to detect and remove material defects as quickly and accurately as possible. Traditional methods of detecting material defects, such as manual inspections or simple visual algorithms, can be unreliable and, above all, time-consuming. In recent years, however, new tools based on intelligent algorithms (Liu et al., 2023) or structures (Neethu et al., 2015), such as neural networks, have emerged that enable automation and significantly increase the precision of defect detection. One of the most popular tools for object detection is the YOLOv5 (Redmon et al., 2016) (You Only Look Once version 5) model, which is based on neural networks and designed for fast and accurate object detection in images. The YOLOv5 model can be trained to detect specific types of material defects, such as stains, cracks, dents, etc. To use the YOLOv5 model for defect detection in materials, images of the material with specified objects of interest are needed, which will be used as input data for the model training. The YOLOv5 model analyses the images and determines rectangular frames around each defect indicating its location, and then assigns a selected label (in this case, the name of the defect). As a result, the YOLOv5 model is able to detect defects in new material images, regardless of their location, colour, and size. As the work progresses, it is also possible to prepare new images and retrain the model for new defects or to improve the performance of existing ones. As a result of the research, the obtained results at a 95% level of recognized defects certainly qualify the applied technology for professional use after appropriate workstation preparation and a sufficiently large amount of data (preferably 1000 or more).

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Role of Computer Vision in Automatic Inspection Systems

Cited by 7 publications

References 4 publications

Text Recognition for Library Collection in Different Light Conditions

Text Recognition for Library Collection in Different Light Conditions

Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions

An Application of a Material Defect Detection System Using Artificial Intelligence

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