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Original Article
Implementation of an Assistive Software for Visually Impaired Individuals
. Divya1
K.S. Hari Priya2
V. Hemalatha3
L. Sharmila4
1 Assistant Professor, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri, Tamil Nadu, India. 2 3 4UG Scholars, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri,Tamil Nadu, India.
Published Online: January-April 2026
Pages: 379-382
Cite this article
No DOIReferences
1. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A., “You Only Look Once: Unified, Real-Time Object Detection,” Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
2. Redmon, J., & Farhadi, A., “YOLO9000: Better, Faster, Stronger,” Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2017.
3. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M., “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint
arXiv:2004.10934, 2020.
4. Jocher, G., et al., “YOLOv5 by Ultralytics,” GitHub Repository, 2020. Available: https://github.com/ultralytics/yolov5
5. He, K., Zhang, X., Ren, S., & Sun, J., “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2016.
6. Ren, S., He, K., Girshick, R., & Sun, J., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE
Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
7. Liu, W., et al., “SSD: Single Shot MultiBox Detector,” Proceedings of the European Conference on Computer Vision (ECCV), 2016.
8. Simonyan, K., & Zisserman, A., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint
arXiv:1409.1556, 2014.
9. OpenCV Documentation, “Open Source Computer Vision Library,” Available: https://docs.opencv.org/
10. Paszke, A., et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” Advances in Neural Information Processing
Systems (NeurIPS), 2019
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
2. Redmon, J., & Farhadi, A., “YOLO9000: Better, Faster, Stronger,” Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2017.
3. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M., “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint
arXiv:2004.10934, 2020.
4. Jocher, G., et al., “YOLOv5 by Ultralytics,” GitHub Repository, 2020. Available: https://github.com/ultralytics/yolov5
5. He, K., Zhang, X., Ren, S., & Sun, J., “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2016.
6. Ren, S., He, K., Girshick, R., & Sun, J., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE
Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
7. Liu, W., et al., “SSD: Single Shot MultiBox Detector,” Proceedings of the European Conference on Computer Vision (ECCV), 2016.
8. Simonyan, K., & Zisserman, A., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint
arXiv:1409.1556, 2014.
9. OpenCV Documentation, “Open Source Computer Vision Library,” Available: https://docs.opencv.org/
10. Paszke, A., et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” Advances in Neural Information Processing
Systems (NeurIPS), 2019
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