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


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

May 5, 2022
July 15, 2022
September 20, 2022
deep neural network, semantic segmentation, object detection, atrous convolution, local features
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.
Data files:
  1. [1] J. Rogelio, E. Dadios, A. Bandala et al., “Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review,” Int. J. of Advances in Intelligent Informatics, Vol.8, No.1, pp. 97-114, 2022.
  2. [2] Golden Engineering Inc., “Portable battery operated X-ray unit Golden Engineering XRS-4,” pp. 6-7.
  3. [3] J. P. Rogelio et al., “Modal Analysis, Computational Fluid Dynamics and Harmonic Response Analysis of a 3D Printed X-ray Film Handler for Assistant Robotic System Using Finite Element Method,” Proc. of the IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), doi: 10.1109/HNICEM51456.2020.9400014, 2020.
  4. [4] A. Kumar, Z. J. Zhang, and H. Lyu, “Object detection in real time based on improved single shot multi-box detector algorithm,” Eurasip J. on Wireless Communications and Networking, Vol.2020, No.1, Article No.204, 2020.
  5. [5] J. Liu and Y. Li, “Visual servoing with deep learning and data augmentation for robotic manipulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 953-962, 2020.
  6. [6] M. Fujitake, M. Inoue, and T. Yoshimi, “Development of an Automatic Tracking Camera System Integrating Image Processing and Machine Learning,” J. Robot. Mechatron., Vol.33, No.6, pp. 1303-1314, 2021.
  7. [7] C. Zhou, H. Yang, J. Zhao et al., “POI classification method based on feature extension and deep learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 944-952, 2020.
  8. [8] D. A. Lisin, M. A. Mattar, M. B. Blaschko et al., “Combining Local and Global Image Features for Object Class Recognition,” IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’05) – Workshops, p. 47, 2005.
  9. [9] R. Mottaghi, X. Chen, X. Liu et al., “The role of context for object detection and semantic segmentation in the wild,” Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 891-898, 2014.
  10. [10] M. Razzok, A. Badri, I. E. Mourabit et al., “A new pedestrian recognition system based on edge detection and different census transform features under weather conditions,” Int. J. of Artificial Intelligence (IJ-AI), Vol.11, No.2, pp. 582-592, 2022.
  11. [11] M. N. Chapel and T. Bouwmans, “Moving objects detection with a moving camera: A comprehensive review,” Computer Science Review, Vol.38, Article No.100310, 2020.
  12. [12] S. Minaee, Y. Y. Boykov, F. Porikli et al., “Image Segmentation Using Deep Learning: A Survey,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.44, No.7, pp. 3523-3542, 2022.
  13. [13] Y. Li, Y. Ma, W. Cai et al., “Complementary convolution residual networks for semantic segmentation in street scenes with deep Gaussian CRF,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.1, pp. 3-12, 2021.
  14. [14] S. Soltan, A. Oleinikov, M. F. Demirci, and A. Shintemirov, “Deep learning-based object classification and position estimation pipeline for potential use in robotized pick-and-place operations,” Robotics, Vol.9, No.3, Article No.63, 2020.
  15. [15] F. Taher and N. Prakash, “Automatic cerebrovascular segmentation methods – A review,” Int. J. of Artificial Intelligence, Vol.10, No.3, pp. 576-583, 2021.
  16. [16] L.-C. Chen, G. Papandreou, F. Schroff et al., “Rethinking Atrous Convolution for Semantic Image Segmentation,” arXiv:1706.05587v3, 2017.
  17. [17] L. Xu, H. Xue, M. Bennamoun et al., “Atrous convolutional feature network for weakly supervised semantic segmentation,” Neurocomputing, Vol.421, pp. 115-126, 2021.
  18. [18] R. Yu, X. Xu, and Z. Wang, “Influence of object detection in deep learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 683-688, 2018.
  19. [19] L. Sun, R. P. Singh, and F. Kanehiro, “Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments,” J. Robot. Mechatron., Vol.33, No.6, pp. 1385-1397, 2021.
  20. [20] H. Wang, B. Yang, J. Wang et al., “Adaptive Visual Servoing of Contour Features,” IEEE/ASME Trans. on Mechatronics, Vol.23, No.2, pp. 811-822, 2018.
  21. [21] P. Patel and A. Thakkar, “The upsurge of deep learning for computer vision applications,” Int. J. of Electrical and Computer Engineering, Vol.10, No.1, pp. 538-548, 2020.
  22. [22] M. Hirabayashi, Y. Saito, K. Murakami et al., “Vision-based sensing systems for autonomous driving: Centralized or decentralized?,” J. Robot. Mechatron., Vol.33, No.3, pp. 686-697, 2021.
  23. [23] A. A. Tulbure, A. A. Tulbure, and E. H. Dulf, “A review on modern defect detection models using DCNNs – Deep convolutional neural networks,” J. of Advanced Research, Vol.35, pp. 33-48, 2022.
  24. [24] J. M. Cho and K. K. Kim, “Precise object detection using local feature for robot manipulator,” Proc. of the 14th Int. Conf. on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 497-499, 2017.
  25. [25] L. Shi, “An Object Detection and Pose Estimation Approach for Position Based Visual Servoing,” Electrical, Control and Communication Engineering, Vol.12, No.1, pp. 34-39, 2017.
  26. [26] P. Kumari and S. R. Naidu, “Fast Approach for Iris Detection on GPU by Applying Search Localization for Circular Hough Transform,” Int. Conf. on Advances in Computing, Communications and Informatics (ICACCI), pp. 774-778, 2018.
  27. [27] X. Tang, X. Wang, J. Hou et al., “An Improved Sobel Face Gray Image Edge Detection Algorithm,” Chinese Control Conf. (CCC), pp. 6639-6643, 2020.
  28. [28] N. B. Youssef, A. Bouzid, and N. Ellouze, “Color image edge detection method based on multiscale product using Gaussian function,” Proc. of the 2016 2nd Int. Conf. on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 228-232, 2016.
  29. [29] B. Du, Z. Hao, and X. Wei, “Roundness Detection of End Face for Shaft Workpiece based on Canny-Zernike Sub Pixel Edge Detection and Improved Hough Transform,” Proc. of IEEE 11th Int. Conf. on Electronics Information and Emergency Communication (ICEIEC), pp. 40-43, 2021.
  30. [30] W. Pan, M. Lyu, K. S. Hwang et al., “A Neuro-Fuzzy Visual Servoing Controller for an Articulated Manipulator,” IEEE Access, Vol.6, pp. 3346-3357, 2018.
  31. [31] T. W. Teng, P. Veerajagadheswar, B. Ramalingam et al., “Vision based wall following framework: A case study with HSR robot for cleaning application,” Sensors, Vol.20, No.11, Article No.3298, 2020.
  32. [32] T. R. Kumar, K. Kalaiselvi, C. M. Veluet et al., “Mammogram Image Segmentation Using Susan Corner Detection,” Proc. of the 2nd Int. Conf. on Electronics and Sustainable Communication Systems (ICESC), pp. 1190-1194, 2021.
  33. [33] L. Juranek, J. Stastny, and V. Skorpil, “Effect of Low-Pass Filters as a Shi-Tomasi Corner Detector’s Window Functions,” Proc. of the 41st Int. Conf. on Telecommunications and Signal Processing (TSP), pp. 623-626, 2018.
  34. [34] C. Chen, Q. Chen, C. Gao et al., “Method of Blob detection based on radon transform,” Proc. of the 30th Chinese Control and Decision Conf. (CCDC), pp. 5762-5767, 2018.
  35. [35] J. Guo, Y. Li, W. Lin et al., “Network decoupling: From regular to depthwise separable convolutions,” Proc. of the 29th British Machine Vision Conf. (BMVC), Article No.248, 2018.
  36. [36] M. Z. Mouffok, H. Tabia, and O. A. Elhara, “Dual Independent Classification for Sketch-Based 3D Shape Retrieval,” Proc. Int. Conf. on Image Processing (ICIP), pp. 2676-2680, 2020.
  37. [37] S. Yallamandaiah and N. Purnachand, “Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors,” Int. J. of Artificial Intelligence, Vol.10, No.4, pp. 1079-1090, 2021.
  38. [38] S. Q. Xie, E. Haemmerle, Y. Cheng et al., “Vision-Guided Robot Control for 3D Object Recognition and Manipulation,” Robot Manipulators, doi: 10.5772/6223, 2008.
  39. [39] B. Debnath, M. O’Brien, M. Yamaguchi et al., “Adapting MobileNets for mobile based upper body pose estimation,” Proc. of the 15th IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance (AVSS), doi: 10.1109/AVSS.2018.8639378, 2019.
  40. [40] A. Kumthekar and G. R. Reddy, “Redesigning U-Net with dense connection and attention module for satellite based cloud detection,” Int. J. of Artificial Intelligence (IJ-AI), Vol.11, No.2, pp. 699-708, 2022.
  41. [41] Z. Li, Y. You, and F. Liu, “Multi-scale ships detection in high-resolution remote sensing image via saliency-based region convolutional neural network,” Int. Geoscience and Remote Sensing Symp. (IGARSS), pp. 246-249, 2019.
  42. [42] A. Farag, L. Lu, H. R. Roth et al., “A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labelling,” IEEE Trans. on Image Processing, Vol.26, No.1, pp. 386-399, 2017.
  43. [43] E. S. Marquez, J. S. Hare, and M. Niranjan, “Deep Cascade Learning,” IEEE Trans. on Neural Networks and Learning Systems, Vol.29, No.11, pp. 5475-5485, 2018.
  44. [44] K. Tong, Y. Wu, and F. Zhou, “Recent advances in small object detection based on deep learning: A review,” Image and Vision Computing, Vol.97, Article No.103910, 2020.
  45. [45] A. Patil and M. Rane, “Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition,” Smart Innovation, Systems and Technologies, Vol.195, pp. 21-30, 2021.
  46. [46] X. Zhang, W. Zhang, J. Peng et al., “Automatic Image Labelling at Pixel Level,” arXiv:2007.07415, 2020.
  47. [47] S. A. Ali and B. G. Prasad, “Scale-Aware Cascading for Semantic Segmentation,” J. of Physics: Conf. Series, Vol.2161, Article No.012016, 2022.
  48. [48] G. Lin, C. Shen, A. van dan Hengel et al., “Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation,” Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3194-3203, 2016.
  49. [49] G. Papandreou, L.-C. Chen, K. P. Murphy et al., “Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation,” Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1742-1750, 2015.
  50. [50] J. Dai, K. He, and J. Sun, “BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation,” Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1635-1643, 2015.
  51. [51] Z. Liu, X. Li, P. Luo et al., “Semantic image segmentation via deep parsing network,” Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1377-1385, 2015.
  52. [52] L.-C. Chen, J. T. Barron, G. Papandreou et al., “Semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform,” Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 4545-4554, 2016.
  53. [53] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,” arXiv:1412.7062, 2014.
  54. [54] L.-C. Chen, G. Papandreou, I. Kokkinos et al., “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.40, No.4, pp. 834-848, 2018.
  55. [55] S. Extension, “Autodesk Highlights Fusion 360 Product Design Capabilities,” More Ansys Coverage, Vol.7, pp. 1-7, 2021.
  56. [56] K. Zhao, D. Nie, Y. Hsu, and E. Tsuchiya, “Mitigation of Repetitive Pattern Effect of Inteltextsuperscript® RealSensetextsuperscript™ Depth Cameras D400 Series,” [accessed May 1, 2022]
  57. [57] M. Everingham, L. V. Gool, C. K. I. Williams et al., “The Pascal Visual Object Classes (VOC) Challenge,” Int. J. of Computer Vision, Vol.88, No.2, pp. 303-338, 2010.
  58. [58] X. Liu, Z. Deng, and Y. Yang, “Recent progress in semantic image segmentation,” Artificial Intelligence Review, Vol.52, No.2, pp. 1089-1106, 2019.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 27, 2022