Object Detection using OpenCV and Python

Sharma, Ayushi; Pathak, Jyotsna; Prakash, Muskan; Singh, Jagpreet

doi:10.1109/icac3n53548.2021.9725638

Cited by 30 publications

(12 citation statements)

References 9 publications

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“…However, similar to previous studies, this model also failed to recognize instances of improperly worn masks. Prominent techniques employed in training face mask detection models involve leveraging pretrained convolutional neural network (CNN) models such as MobileNetV2 or InceptionV3, or even a strategic fusion of both [13].…”

Section: Face Mask Recognitionmentioning

confidence: 99%

Implementation of Mask Use Detection With SVM and Haar Cascade in OpenCV

Hustinawaty,

Muhammad Farell

2024

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Despite a decline in global COVID-19 cases, the persisting threat of SARS-CoV-2 coupled with waning public awareness of the virus threat has raised concerns. A notable number of individuals disregard mask usage or do so incorrectly. It is particularly concerning given that COVID-19 has high transmissibility, especially in crowded areas like shopping centers. Enforcement officers often face challenges in identifying those wearing masks improperly. Herein lies the significance of automated mask detection to aid enforcement officers in containing the spread of the virus. Hence, this paper aims to highlight the importance of automated mask detection in combatting COVID-19 transmission. Previous mask detection algorithms were intricate because they relied heavily on resource-intensive machine learning algorithms and libraries. These algorithms, however, failed to address the problem of incorrect mask usage adequately. Therefore, despite the apparent usage of masks, the virus managed to find transmission pathways. In contrast, this research focuses on creating algorithms that pinpoint improper mask usage and optimize resource utilization without compromising detection quality. The Haar cascade algorithm was utilized to detect faces and the support vector machine (SVM) was used to train the dataset. The model attained an average accuracy of 95.8%, precision of 99.7%, recall of 92.3%, and F1-score of 93.7%. The metrics aligned with prior studies, affirming their reliability. Nevertheless, limitations exist as the model faces challenges in detecting obscured facial features, requiring further research to enhance its detection capabilities. This research contributes to ongoing efforts to improve mask detection technology for more effective virus containment.

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Section: Face Mask Recognitionmentioning

confidence: 99%

Implementation of Mask Use Detection With SVM and Haar Cascade in OpenCV

Hustinawaty,

Muhammad Farell

2024

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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“…Its comprehensive suite of image-processing functions facilitates the parsing of continuous video streams into individual frames, allowing for diverse operations such as filtering, transformation, and enhancement. The capability for frame-by-frame analysis is particularly valuable in scenarios requiring a detailed inspection, as commonly encountered in assembly processes [53][54][55].…”

Section: Real-time Processingmentioning

confidence: 99%

Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study

Silva,

Coelho,

Saraiva

et al. 2024

Applied Sciences

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Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.

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“…Digital image processing is carried out to correct image signal data errors that occur due to transmission and during signal acquisition, as well as to improve the quality of image appearance so that it is easier for the human visual system to interpret by manipulating and analyzing images. Operations performed to transform an image into another embodiment can be grouped based on the purpose of the transformation or the scope of operations served on the image [18].…”

Section: Open CVmentioning

confidence: 99%

Human Object Detection for Real-Time Camera using Mobilenet-SSD

Muwardi,

Permana,

Gao

et al. 2023

JIAE

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Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can benefit from this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is safety, namely in searching for people, which is still done manually. Searching for teams to find people is often challenging because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the MobilenetV2-SSD, where this algorithm has high detection and accuracy. Using the mobilenetV2-SSD simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.

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Object Detection using OpenCV and Python

Cited by 30 publications

References 9 publications

Implementation of Mask Use Detection With SVM and Haar Cascade in OpenCV

Implementation of Mask Use Detection With SVM and Haar Cascade in OpenCV

Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study

Human Object Detection for Real-Time Camera using Mobilenet-SSD

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