Research Article
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Year 2025, Volume: 13 Issue: 1, 17 - 21, 31.01.2025
https://doi.org/10.21541/apjess.1542885

Abstract

References

  • The Editors of Encyclopedia Britannica, “Image processing”, Encyclopedia Britannica, Accessed on: Feb. 27, 2023. [Online]. Available: https://www.britannica.com/technology/image-processing
  • S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, "Image segmentation using deep learning: A survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 2021. [Online]. Available: https://arxiv.org/pdf/2001.05566.pdf
  • Computer Vision Foundation Videos, "Mask R-CNN," YouTube, Nov. 17, 2017. [Online]. Available: https://www.youtube.com/watch?v=g7z4mkfRjI4
  • Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye, "Object Detection in 20 Years: A Survey," 2023. [Online]. Available: https://arxiv.org/abs/1905.05055v3
  • R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," 2014. [Online]. Available: https://arxiv.org/abs/1311.2524
  • R. Girshick, "Fast R-CNN," 2015. [Online]. Available: https://arxiv.org/abs/1504.08083v2
  • S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," 2016. [Online]. Available: https://arxiv.org/abs/1506.01497v3
  • K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," 2018. [Online]. Available: https://arxiv.org/abs/1703.06870v3
  • J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
  • M. Hussain, "YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection," Machines, vol. 11, 2023, Art. no. 677. [Online]. Available: https://doi.org/10.3390/machines11070677
  • R. Sapkota, D. Ahmed, and M. Karkee, "Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments," Artificial Intelligence in Agriculture, vol. 13, pp. 84–99, 2024. [Online]. Available: https://doi.org/10.1016/j.aiia.2024.07.001
  • R. Olmos, S. Tabik, and F. Herrera, "Automatic handgun detection alarm in videos using deep learning," Neurocomputing, vol. 275, pp. 66-72, 2018. [Online]. Available: doi: 10.1016/j.neucom.2017.05.012
  • J. Salido, V. Lomas, J. Ruiz-Santaquiteria, and O. Deniz, "Automatic handgun detection with deep learning in video surveillance images," Applied Sciences, vol. 11, no. 13, p. 6085, 2021. [Online]. Available: doi: 10.3390/app11136085
  • A. A. Ahmed and M. Echi, "Hawk-eye: An AI-powered threat detector for intelligent surveillance cameras," IEEE Access, vol. 9, pp. 63283-63293, 2021.
  • A. Goenka and K. Sitara, "Weapon Detection from Surveillance Images using Deep Learning," in 3rd International Conference for Emerging Technology (INCET), 2022. pp. 1-6. [Online]. Available: doi: 10.1109/INCET54531.2022.9824281
  • S. Khalid, A. Waqar, H. U. Ain Tahir, O. C. Edo, and I. T. Tenebe, "Weapon detection system for surveillance and security," in 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD), Manama, Bahrain, 2023. pp. 1-7. [Online]. Available: doi: 10.1109/ITIKD56332.2023.10099733
  • O. Veranyurt and C. O. Sakar, "Concealed pistol detection from thermal images with deep neural networks," Multimed Tools Appl, vol. 82, pp. 44259–44275, 2023. [Online]. Available: doi: 10.1007/s11042-023-15358-1
  • Y. Huang, X. Fu, and Y. Zeng, "Anchor-Free Weapon Detection for X-Ray Baggage Security Images," IEEE Access, vol. 10, pp. 97843-97855, 2022.
  • W. Abdulla, "Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow," GitHub, 2017. [Online]. Available: https://github.com/matterport/Mask_RCNN
  • T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, "Microsoft COCO: Common Objects in Context," 2015. [Online]. Available: https://arxiv.org/abs/1405.0312v3
  • R. Padilla, S. L. Netto, and E. A. B. da Silva, "A Survey on Performance Metrics for Object-Detection Algorithms," in Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niteroi, Brazil, 2020.
  • R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. da Silva, "A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit," Electronics, vol. 10, p. 279, 2021.

Modeling Objects with Artificial Intelligence Based Image Processing Techniques: Object Detection with Mask R-CNN

Year 2025, Volume: 13 Issue: 1, 17 - 21, 31.01.2025
https://doi.org/10.21541/apjess.1542885

Abstract

Object detection and classification on digital images have great importance in the digitalizing world. After deep learning methods started being implemented for object detection, classification and segmentation a rapid development has been monitored in the field. One of the most successful methods in the field is Mask R-CNN. It can be used in order to detect and segment purposes for many different objects. This study contains the use of Mask R-CNN for weapon detection, specifically handguns. Nowadays, there are many cameras in public areas and these cameras can detect weapons before a forensic incident. Our model achieved a mean average precision (mAP) of 0.78 in the detection of handguns on test data. Our findings verify the potential of deep learning in security by detecting threats in images and live videos.

Ethical Statement

This article is an extended version of an unpublished paper titled 'Modeling Objects With Artificial Intelligence Based Image Processing Techniques: Handgun Detection With MASK R-CNN' presented at the 10th International Conference on Management Information Systems on 18-20 October 2023.

References

  • The Editors of Encyclopedia Britannica, “Image processing”, Encyclopedia Britannica, Accessed on: Feb. 27, 2023. [Online]. Available: https://www.britannica.com/technology/image-processing
  • S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, "Image segmentation using deep learning: A survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 2021. [Online]. Available: https://arxiv.org/pdf/2001.05566.pdf
  • Computer Vision Foundation Videos, "Mask R-CNN," YouTube, Nov. 17, 2017. [Online]. Available: https://www.youtube.com/watch?v=g7z4mkfRjI4
  • Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye, "Object Detection in 20 Years: A Survey," 2023. [Online]. Available: https://arxiv.org/abs/1905.05055v3
  • R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," 2014. [Online]. Available: https://arxiv.org/abs/1311.2524
  • R. Girshick, "Fast R-CNN," 2015. [Online]. Available: https://arxiv.org/abs/1504.08083v2
  • S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," 2016. [Online]. Available: https://arxiv.org/abs/1506.01497v3
  • K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," 2018. [Online]. Available: https://arxiv.org/abs/1703.06870v3
  • J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788.
  • M. Hussain, "YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection," Machines, vol. 11, 2023, Art. no. 677. [Online]. Available: https://doi.org/10.3390/machines11070677
  • R. Sapkota, D. Ahmed, and M. Karkee, "Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments," Artificial Intelligence in Agriculture, vol. 13, pp. 84–99, 2024. [Online]. Available: https://doi.org/10.1016/j.aiia.2024.07.001
  • R. Olmos, S. Tabik, and F. Herrera, "Automatic handgun detection alarm in videos using deep learning," Neurocomputing, vol. 275, pp. 66-72, 2018. [Online]. Available: doi: 10.1016/j.neucom.2017.05.012
  • J. Salido, V. Lomas, J. Ruiz-Santaquiteria, and O. Deniz, "Automatic handgun detection with deep learning in video surveillance images," Applied Sciences, vol. 11, no. 13, p. 6085, 2021. [Online]. Available: doi: 10.3390/app11136085
  • A. A. Ahmed and M. Echi, "Hawk-eye: An AI-powered threat detector for intelligent surveillance cameras," IEEE Access, vol. 9, pp. 63283-63293, 2021.
  • A. Goenka and K. Sitara, "Weapon Detection from Surveillance Images using Deep Learning," in 3rd International Conference for Emerging Technology (INCET), 2022. pp. 1-6. [Online]. Available: doi: 10.1109/INCET54531.2022.9824281
  • S. Khalid, A. Waqar, H. U. Ain Tahir, O. C. Edo, and I. T. Tenebe, "Weapon detection system for surveillance and security," in 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD), Manama, Bahrain, 2023. pp. 1-7. [Online]. Available: doi: 10.1109/ITIKD56332.2023.10099733
  • O. Veranyurt and C. O. Sakar, "Concealed pistol detection from thermal images with deep neural networks," Multimed Tools Appl, vol. 82, pp. 44259–44275, 2023. [Online]. Available: doi: 10.1007/s11042-023-15358-1
  • Y. Huang, X. Fu, and Y. Zeng, "Anchor-Free Weapon Detection for X-Ray Baggage Security Images," IEEE Access, vol. 10, pp. 97843-97855, 2022.
  • W. Abdulla, "Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow," GitHub, 2017. [Online]. Available: https://github.com/matterport/Mask_RCNN
  • T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, "Microsoft COCO: Common Objects in Context," 2015. [Online]. Available: https://arxiv.org/abs/1405.0312v3
  • R. Padilla, S. L. Netto, and E. A. B. da Silva, "A Survey on Performance Metrics for Object-Detection Algorithms," in Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niteroi, Brazil, 2020.
  • R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. da Silva, "A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit," Electronics, vol. 10, p. 279, 2021.
There are 22 citations in total.

Details

Primary Language English
Subjects Deep Learning
Journal Section Research Articles
Authors

Ömer Faruk Ereken 0000-0003-3041-9665

Çiğdem Tarhan 0000-0002-5891-0635

Publication Date January 31, 2025
Submission Date September 3, 2024
Acceptance Date December 28, 2024
Published in Issue Year 2025 Volume: 13 Issue: 1

Cite

IEEE Ö. F. Ereken and Ç. Tarhan, “Modeling Objects with Artificial Intelligence Based Image Processing Techniques: Object Detection with Mask R-CNN”, APJESS, vol. 13, no. 1, pp. 17–21, 2025, doi: 10.21541/apjess.1542885.

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