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JACIII Vol.26 No.5 pp. 842-850
doi: 10.20965/jaciii.2022.p0842
(2022)

Paper:

Object Detection and Segmentation Using Deeplabv3 Deep Neural Network for a Portable X-Ray Source Model

Jayson P. Rogelio*,**,†, Elmer P. Dadios***, Ryan Ray P. Vicerra***, and Argel A. Bandala**

*Department of Science and Technology, Metals Industry Research and Development Center
General Santos Ave., Bicutan, Taguig 1631, Philippines

**Department of Electronics and Computer Engineering, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

***Department of Manufacturing Engineering and Management, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

Received:
May 5, 2022
Accepted:
July 15, 2022
Published:
September 20, 2022
Keywords:
deep neural network, semantic segmentation, object detection, atrous convolution, local features
Abstract
Object Detection and Segmentation Using Deeplabv3 Deep Neural Network for a Portable X-Ray Source Model

Deeplabv3 for 3D X-ray source model

The primary purpose of this research is to implement Deeplabv3 architecture’s deep neural network in detecting and segmenting portable X-ray source model parts such as body, handle, and aperture in the same color scheme scenario. Similarly, the aperture is smaller with lower resolution making deep convolutional neural networks more difficult to segment. As the input feature map diminishes as the net progresses, information about the aperture or the object on a smaller scale may be lost. It recommends using Deeplabv3 architecture to overcome this issue, as it is successful for semantic segmentation. Based on the experiment conducted, the average precision of the body, handle, and aperture of the portable X-ray source model are 91.75%, 20.41%, and 6.25%, respectively. Moreover, it indicates that detecting the “body” part has the highest average precision. In contrast, the detection of the “aperture” part has the lowest average precision. Likewise, the study found that using Deeplabv3 deep neural network architecture, detection, and segmentation of the portable X-ray source model was successful but needed improvement to increase the overall mean AP of 39.47%.

Cite this article as:
J. Rogelio, E. Dadios, R. Vicerra, and A. Bandala, “Object Detection and Segmentation Using Deeplabv3 Deep Neural Network for a Portable X-Ray Source Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.5, pp. 842-850, 2022.
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