In object detection deep learning methods, YOLO shows supremum to Mask R-CNN

Sumit, S.S. and Watada, J. and Roy, A. and Rambli, D.R.A. (2020) In object detection deep learning methods, YOLO shows supremum to Mask R-CNN. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Deep learning concept and algorithm play a pivotal role in solving various complicated problems such as playing games, forecasting economic future values, detecting objects in images. It could break through the bottle neck in conventional methods of neural networks and artificial intelligence. This paper will compare two influential deep learning algorithms in image processing and object detection, that is, Mask R-CNN and YOLO. Today, detection tasks become more complex when they come to numerous variations in the humans' perceived appearance, formation, attire, reasoning and the dynamic nature of their behaviour. It is also a challenging task to understand subtle details in their surroundings. For instance, radiance conditions, background clutter and partial or full occlusion. When a machine tries to interact with human or try to take pictures, it becomes hard for them to magnify the details of a human surrounding. In this study we have focused to detect humans effectively. The main objective of the present work is to compare the performance of YOLO and Mask R-CNN, which unveils the inability of Mask R-CNN in detecting tiny human figures among other prominent human images, and illustrate YOLO was successful in detecting most of the human figures in an image with higher accuracy. Therefore, the paper evaluates and differentiates the performance of YOLO from the deep learning method Mask R-CNN in two points, (1) detection ability and (2) computation time. Since, the machine learning algorithms are mostly data specific, the authors believe that the presented results might vary with the varying nature of the data under observation. In another way, the presented data might be seen as a counter example of unveiling the detection inaccuracy of the Mask R-CNN. © Published under licence by IOP Publishing Ltd.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 30; Conference of 2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019 ; Conference Date: 25 November 2019 Through 27 November 2019; Conference Code:161273
Uncontrolled Keywords: Bottles; Convolutional neural networks; Deep learning; Learning systems; Object detection; Object recognition; Sports, Background clutter; Computation time; Conventional methods; Counter examples; Detecting objects; Detection ability; Economic future; Learning methods, Learning algorithms
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:27
Last Modified: 10 Nov 2023 03:27
URI: https://khub.utp.edu.my/scholars/id/eprint/13007

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