Comparative Analysis on YOLO Object Detection with OpenCV

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Deshpande H.; SINGH A.; Herunde H.

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Paper Information

Journal: INTERNATIONAL JOURNAL OF RESEARCH IN INDUSTRIAL ENGINEERING Year:2020 | Volume:9 | Issue:1 Page(s): 46-64

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Information Journal Paper

Title

Comparative Analysis on YOLO Object Detection with OpenCV

Author(s)

Deshpande H. | SINGH A. | Herunde H. | Issue Writer Certificate

Keywords

YOLO

Faster-RCNN

Convolutional neural network

COCO

Abstract

Computer Vision is a field of study that helps to develop techniques to identify images and displays. It has various features like image recognition, object detection and image creation, etc. Object detection is used for face detection, vehicle detection, web images, and safety systems. Its algorithms are Region-based Convolutional neural networks (RCNN), Faster-RCNN and You Only Look Once Method (YOLO) that have shown state-of-the-art performance. Of these, YOLO is better in speed compared to accuracy. It has efficient object detection without compromising on performance.

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APA: Copy

Deshpande, H., SINGH, A., & Herunde, H.. (2020). Comparative Analysis on YOLO Object Detection with OpenCV. INTERNATIONAL JOURNAL OF RESEARCH IN INDUSTRIAL ENGINEERING, 9(1), 46-64. SID. https://sid.ir/paper/768504/en

Vancouver: Copy

Deshpande H., SINGH A., Herunde H.. Comparative Analysis on YOLO Object Detection with OpenCV. INTERNATIONAL JOURNAL OF RESEARCH IN INDUSTRIAL ENGINEERING[Internet]. 2020;9(1):46-64. Available from: https://sid.ir/paper/768504/en

IEEE: Copy

H. Deshpande, A. SINGH, and H. Herunde, “Comparative Analysis on YOLO Object Detection with OpenCV,” INTERNATIONAL JOURNAL OF RESEARCH IN INDUSTRIAL ENGINEERING, vol. 9, no. 1, pp. 46–64, 2020, [Online]. Available: https://sid.ir/paper/768504/en

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