International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

Car detection area segmentation using deep learning system

  • Dong-Jin Kwon (Department of Computer Electronics Engineering, Seoil University) ;
  • Sang-hoon Lee (Korea Institute of Science and Technology)
  • Received : 2023.10.19
  • Accepted : 2023.10.29
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

Keywords

Acknowledgement

The present research has been conducted by the Research Grant of Seoil University

References

  1. K. He, G. Gkioxari, P. Dollar, and Ross B. Girshick, "Mask R-CNN," , ICCV 2017. DOI : https://doi.org/10.1109/ICCV.2017.322
  2. J. Redmon et al., "You Only Look Once: Unified, Real-Time Object Detection", pp. 779-788, CVPR, 2016. DOI : https://doi.org/10.1109/CVPR.2016.91
  3. J. Redmon, A. Farhadi, "YOLO9000: Better, Faster, Stronger", CVPR, 2017. DOI : https://doi.org/10.1109/CVPR.2017.690
  4. J. Redmon, Ali Farhadi, "YOLOv3: An Incremental Improvement", arXiv, 2018
  5. T. Dettmers, "Benchmarking state-of-the-art deep learning software tools", arXiv, 2016. 
  6. S. Mittal, J. S Vetter, "A survey of CPU-GPU heterogeneous computing techniques", CSUR, 2015. DOI : https://doi.org/10.1145/2788396
  7. J G. Ellis, D. Joshi, et al, "Region-based Image Retrieval with Revisited", arXiv, 2017.
  8. S. Chetlur, et al, "cuDNN: Efficient Primitives for Deep Learning", arXiv, 2014.
  9. Y. Zhu, et al, "Instance-aware Semantic Segmentation via Multi-task Network Cascades", CVPR, 2017. DOI : https://doi.org/10.1109/CVPR.2016.343
  10. Y. LeCun, Y. Bengio and G. Hinton, "Deep Learning", nature, 2015. DOI : https://doi.org/10.1038/nature14539 
  11. J. Long, E. Shelhamer and T. Darrel, "Fully Convolutional Networks for Sementic Segmentation", CVPR, 2015. DOI : https://doi.org/10.1109/TPAMI.2016.2572683 ,
  12. V. V. Zunin, "Intel OpenVINO Toolkit for Computer Vision: Object Detection and Semantic Segmentation" , 2021  International Russian Automation Conference (RusAutoCon) , Sep 2021.  DOI : https://doi.org/10.1109/RusAutoCon52004.2021.9537452
  13. M. Mahrishi, S. Morwal, A. Wahab Muzaffar, S. Bhatia, P. Dadheech, M. Khalid Imam Rahmani, "Video Index  Point Detection and Extraction Framework Using Custom YoloV4 Darknet Object Detection Model", Volume: 9,  IEEE, Oct 2021.  DOI : https://doi.org/10.1109/ACCESS.2021.3118048
  14. C. Yao, W. Liu, W. Tang, J. Guo, S. Hu, Y. Lu, W. Jiang, "Evaluating and analyzing the energy efficiency of CNN  inference on high-performance GPU" , Wiley Online Library, Oct 2020.  DOI : https://doi.org/10.1002/cpe.6064 
  15. N. Shrivastava, V. Tyagi, "A Review of ROI Image Retrieval Techniques", volume: 328, AISC, 2015. 
  16. W Rong, Z Li, W Zhang, L Sun, "An improved CANNY edge detection algorithm" IEEE, Aug 2014.  DOI : https://doi.org/10.1109/ICMA.2014.6885761
  17. A. Sharma; K. Shah; S. Verma, "Face Recognition using Haar Cascade and Local Binary Pattern Histogram in  OpenCV", 2021 Sixth International Conference on Image Information Processing (ICIIP) , Nov 2021.  DOI : https://doi.org/10.1109/ICIIP53038.2021.9702579