ENHANCING ROBOTIC PERCEPTION THROUGH DEEP LEARNING-BASED OBJECT DETECTION
Keywords:
Robotics, Computer vision, Deep learning, Object detection, Faster R-CNN, Robotic perception, YOLO.Abstract
Robotic systems heavily rely on accurate perception of their environment to navigate, manipulate objects, and interact with the world. Computer vision, particularly deep learning-based object detection, has revolutionized how robots "see" and understand their surroundings. This article explores the advancements in object detection algorithms, such as Faster R-CNN, YOLO, and SSD, and their applications in robotics. These algorithms are not only improving the speed and accuracy of object recognition for robots but also enhancing their ability to handle diverse environments and objects. We delve into how these advancements are enabling a wide range of tasks, from warehouse automation to autonomous vehicles, and discuss their implications for the field of robotics. Additionally, we highlight the challenges in deploying deep learning-based perception systems on robots, such as real-time processing constraints, data efficiency, and domain adaptation. Through case studies and examples, we showcase the effectiveness of deep learning in robotic perception and its role in shaping the future of robotics. This article is aimed at researchers, engineers, and enthusiasts interested in the intersection of computer vision and robotics, providing insights into the state-of-the-art methods and future directions in enhancing robotic perception through deep learning-based object detection.
References
1. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (pp. 91-99).
2. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
3. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., & Reed, S. (2016). SSD: Single shot multibox detector. In European conference on computer vision (pp. 21-37).
4. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Berg, A. C. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252.
5. Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
6. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv:2004.10934.
7. Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848-6856).
8. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
9. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2014). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229.
10. Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).
11. Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
12. Kukharets, K., Salov, Y., & Kulagin, V. (2020). Object Detection and Semantic Segmentation on Industrial Robots Based on Convolutional Neural Networks. In 2020 5th International Scientific Conference "Intelligent Information Technologies for Industry" (IITI) (pp. 1-5).
13. Sodikjanov J.Sh., “Ga1-xAlxAs nanostructures grown on the GaAs Surface by Ion Implantation”, Texa.Jour. of Engg. and Tech., vol. 20, pp. 9–13, May 2023. URL: https://zienjournals.com/index.php/tjet/article/view/3907
14. Sodikjanov Jaxongirbek and Murotova Zulfizar, “bibliometric analysis of intelligent management of street lighting systems”, ITSE, vol. 2, no. 8, pp. 450–464, Apr. 2023. URL: https://humoscience.com/index.php/itse/article/view/607 DOI: https://doi.org/10.5281/zenodo.7833106
15. J.Sh. Sodikjanov and Q.A.Khayitboyev, “Research of Software-Hardware of Industrial Robots”, Texa.Jour. of Engg. and Tech., vol. 22, pp. 13–16, Jul. 2023. URL: https://zienjournals.com/index.php/tjet/article/view/4225
16. Содикжанов Жахонгирбек Шухратбек угли, “упругие и неупругие столкновения в одном и двух измерениях”, ITSE, vol. 2, no. 7, pp. 118–127, Mar. 2023. URL: https://humoscience.com/index.php/itse/article/view/329 DOI: https://doi.org/10.5281/zenodo.7710483
