Analysis and Implementation of an Efficient Traffic Sign Recognition Based on YOLO and SIFT for Turtlebot3 Robot

Menghani, Stefan

doi:10.25673/112993

Proceedings of International Conference on Applied Innovation in IT
2023/11/30, Volume 11, Issue 2, pp.47-58

Analysis and Implementation of an Efficient Traffic Sign Recognition Based on YOLO and SIFT for Turtlebot3 Robot

Stefan Twieg and Ravin Menghani

Abstract: Traffic Sign Recognition (TSR) is one of the key aspects for autonomous driving and it plays a vital role to make autonomous driving successful, but that’s only possible if TSR is efficient enough and reliable. This work addresses exploration of simple and fast to implement options for robotic applications. For analysis and implementation, we are focusing on a Turtlebot3 Robot (TB3). Various potential TSR algorithms are evaluated in different test-cases with the goal of developing an optimized TSR with accurate results for German traffic signs. Therefore, the robot was tested on its own Mini-City track. On this Track we started to detect the signs with a simple Scale-Invariant Feature Transform (SIFT). However, the accuracy of SIFT was showing limitations for the use within TSR on mini-city-Track. This approach focuses on educational use where limitations and simple applications of autonomous driving are investigated. A review of state-of-art algorithms was done, to evaluate and improve accuracy. For example, Oriented FAST and Rotated Brief algorithm (ORB), You Only Look Once (YOLO) and SIFT algorithm was tested on TB3 in a way that all important criteria are fulfilled along with system being real-time. Regarding YOLOv8 a custom dataset and training is performed. The YOLO-model achieves 99.5% in terms of mean Average Perception(mAP@0.5) for all classes. In summary, as a powerful alternative to work with, YOLOv8 was identified. Standalone or in combination with SIFT a TSR system is shown which can work impacted by several environmental conditions. Based on evaluation of three algorithms an optimized code was developed in which YOLOv8 and SIFT were used in combination as a well performing TSR algorithm, which has above 95% accuracy for each traffic sign tested.

Keywords: Traffic Sign Recognition, Machine Learning, YOLO, SIFT, Turtlebot3, ROS, Robot Operating System, Convolutional-Neural-Network, CNN.

DOI: 10.25673/112993

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References:

"ROBOTIS e-Manual," Online]. Available: https://emanual.robotis.com/docs/en/platform/ turtlebot3/autonomous_driving/#traffic-sign-detection, [Accessed Sep. 7, 2023].
R. Amsters and P. Slaets, "Turtlebot 3 as a Robotics Education Platform," in Robotics in Education (Advances in Intelligent Systems and Computing), M. Merdan, W. Lepuschitz, G. Koppensteiner, R. Balogh, and D. Obdržálek, Eds., Cham: Springer International Publishing, 2020, pp. 170-181.
B. Zhong and Y. Li. "Image Feature Point Matching Based on Improved SIFT Algorithm," [Accessed Aug. 22, 2023].
V. Vijayan and P. Kp. "FLANN Based Matching with SIFT Descriptors for Drowsy Features Extraction," [Accessed Aug. 22, 2023].
M. A. A. Babiker, M. A. O. Elawad, and A. H. M. Ahmed, "Convolutional Neural Network for a Self-Driving Car in a Virtual Environment," 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan, 2019, pp. 1-6, doi: 10.1109/ICCCEEE46830.2019.9070826.
J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," Apr. 2018, [Online]. Available: https://arxiv.org/pdf/1804.02767.
J. Terven and D. Cordova-Esparza, "A Comprehensive Review of YOLO: From YOLOv1 and Beyond," Apr. 2023, [Online]. Available: https://arxiv.org/pdf/2304.00501.
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, no. 7, p. 677, 2023, doi: 10.3390/machines11070677.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, Jun. 2014 - Jun. 2014, pp. 580-587, doi: 10.1109/CVPR.2014.81.
H. Yanagisawa, T. Yamashita, and H. Watanabe, "A study on object detection method from manga images using CNN," in 2018 International Workshop on Advanced Image Technology (IWAIT), 2018, pp. 1-4, doi: 10.1109/IWAIT.2018.8369633.
O. Hmidani and E. M. Ismaili Alaoui, "A comprehensive survey of the R-CNN family for object detection," 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), Marrakech, Morocco, 2022, pp. 1-6, doi: 10.1109/CommNet56067.2022.9993862.
J. Du, "Understanding of Object Detection Based on CNN Family and YOLO," J. Phys.: Conf. Ser., vol. 1004, no. 1, p. 12029, 2018, doi: 10.1088/1742-6596/1004/1/012029.
E. Karami, M. Shehata, and A. Smith, "Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations," Oct. 2017, [Online]. Available: https://arxiv.org/pdf/ 1710.02728.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. "ORB: An efficient alternative to SIFT or SURF," [Accessed Aug. 22, 2023].
F. K. Noble, "Comparison of OpenCV's feature detectors and feature matchers,” in The proceedings of 23rd International Conference on Mechatronics and Machine Vision in Practice: M2VIP 2016 : Nov. 28-30, 2016, Nanjing, Jiangsu, China, Nanjing, China, J. Potgieter, P. Xu, Z.-S. Zhang, X.-S. Wang, H. Yi, and I. C. o. M. a. M. V. i. Practice, Eds., 2016, pp. 1-6, doi: 10.1109/M2VIP.2016.7827292.
Sh. Nimmisha, "Classification of stages of Diabetic Retinopathy using Deep Learning," 2020, doi: 10.13140/RG.2.2.10503.62883.
"Quickstart - Ultralytics YOLOv8 Docs," [Online]. Available: https://docs.ultralytics.com/quickstart/ #use-ultralytics-with-cli, [Accessed Sep. 12, 2023].
S. Ola, Th. Bjørsum-Meyer, A. Histace, G. Baatrup, and A. Koulaouzidis, "Annotation Tools in Gastrointestinal Polyp Annotation" Diagnostics 12, no. 10: 2324, 2022, [Online]. Available: https://doi.org/10.3390/ diagnostics12102324
M. Shoeb, M. Akram Ali, M. Shadeel, and M. Abdul Bari, "Self-Driving Car: Using Opencv2 and Machine Learning," The International journal of analytical and experimental modal analysis (IJAEMA), ISSN 0886-9367.
G. Rossum and F.L. Drake, "Python 3 Reference Manual", Scotts Valley, CA: CreateSpace, 2009.

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DOI: http://dx.doi.org/10.25673/115729

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