Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5

Yıl 2024, Cilt: 28 Sayı: 2, 418 - 430, 30.04.2024

Gülyeter Öztürk , Osman Eldoğan , Raşit Köker

https://doi.org/10.16984/saufenbilder.1393307

Cited By: 1

Öz

There has been a global increase in the number of vehicles in use, resulting in a higher occurrence of traffic accidents. Advancements in computer vision and deep learning enable vehicles to independently perceive and navigate their environment, making decisions that enhance road safety and reduce traffic accidents. Worldwide accidents can be prevented in both driver-operated and autonomous vehicles by detecting living and inanimate objects such as vehicles, pedestrians, animals, and traffic signs in the environment, as well as identifying lanes and obstacles. In our proposed system, road images are captured using a camera positioned behind the front windshield of the vehicle. Computer vision techniques are employed to detect straight or curved lanes in the captured images. The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. We use a combination of study-specific images and the GRAZ dataset in our research. In the object detection study, which involves 10 different objects, we evaluate the performance of five different versions of the YOLOv5 model. Our evaluation metrics include precision, recall, precision-recall curves, F1 score, and mean average precision. The experimental results clearly demonstrate the effectiveness of our proposed lane detection and object detection method.

Anahtar Kelimeler

YOLOv5, Object Detection, Lane Detection, Computer Vision, Deep Learning

Kaynakça

• [1] R. Muthalagu, A. S. Bolimera, D. Duseja, S. Fernandes, “Object and Lane Detection Technique for Autonomous Car Using Machine Learning Approach,” Transport and Telecommunication, vol. 22, no. 4, pp. 383–391, 2021.
• [2] V. Nguyen, H. Kim, S. Jun, K. Boo, “A Study on Real-Time Detection Method of Lane and Vehicle for Lane Change Assistant System Using Vision System on Highway,” Engineering Science and Technology, an International Journal, vol. 21, no. 5, pp. 822–833, 2018.
• [3] H. G. Zhu, “An Efficient Lane Line Detection Method Based on Computer Vision,” Journal of Physics: Conference Series, vol. 1802, no. 3, 2021, p. 032006.
• [4] G. Ji, Y. Zheng, “Lane Line Detection System Based on Improved Yolo V3 Algorithm,” In Review, preprint, 2021.
• [5] B. Dorj, S. Hossain, D.-J. Lee, “Highly Curved Lane Detection Algorithms Based on Kalman Filter,” Applied Sciences, vol. 10, no. 7:2372, 2020.
• [6] X. Yan, Y. Li, “A method of lane edge detection based on Canny algorithm,” in Chinese Automation Congress (CAC), Jinan, China, 2017, pp. 2120–2124.
• [7] M. L. Talib, X. Rui, K. H. Ghazali, N. Mohd. Zainudin, S. Ramli, “Comparison of Edge Detection Technique for Lane Analysis by Improved Hough Transform,” in Advances in Visual Informatics, H. B. Zaman, P. Robinson, P. Olivier, T. K. Shih, and S. Velastin, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2013, pp. 176–183.
• [8] Q. Zou, H. Jiang, Q. Dai, Y. Yue, L. Chen, Q. Wang, “Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 41–54, 2020.
• [9] T. M. Hoang, H. G. Hong, H. Vokhidov, K. R. Park, “Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor,” Sensors, vol. 16, no. 8, 2016.
• [10] Y. Li, W. Zhang, X. Ji, C. Ren, J. Wu, “Research on Lane a Compensation Method Based on Multi-Sensor Fusion,” Sensors, vol. 19, no. 7, 2019.
• [11] J. Wang, H. Ma, X. Zhang, X. Liu, “Detection of Lane Lines on Both Sides of Road Based on Monocular Camera,” in 2018 IEEE International Conference on Mechatronics and Automation (ICMA), 2018, pp. 1134–1139.
• [12] S. Kumar, M. Jailia, S. Varshney, “An efficient approach for highway lane detection based on the Hough transform and Kalman filter,” Innovative Infrastructure Solutions, vol. 7, no. 5, p. 290, 2022.
• [13] A. Dubey, K. M. Bhurchandi, “Robust and Real Time Detection of Curvy Lanes (Curves) with Desired Slopes for Driving Assistance and Autonomous Vehicles,” in International Conference on Signal and Image Processing (AIRCC), 2015.
• [14] Y. Huang, Y. Li, X. Hu, W. Ci, “Lane Detection Based on Inverse Perspective Transformation and Kalman Filter,” KSII Transactions on Internet and Information Systems, TIIS, vol. 12, no. 2, pp. 643–661, 2018.
• [15] A. Shustanov, P. Yakimov, “CNN Design for Real-Time Traffic Sign Recognition,” Procedia Engineering., vol. 201, pp. 718–725, 2017.
• [16] I. Kilic, G. Aydin, “Traffic Sign Detection and Recognition Using TensorFlow’ s Object Detection API With A New Benchmark Dataset,” in 2020 International Conference on Electrical Engineering (ICEE), Istanbul, Turkey, 2020, pp. 1–5.
• [17] R. Wang, Z. Wang, Z. Xu, C. Wang, Q. Li, Y. Zhang, H. Li, “A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4,” Computational Intelligence and Neuroscience, vol. 2021, p. e9218137, 2021.
• [18] Z. Yang, J. Li, H. Li, “Real-time Pedestrian and Vehicle Detection for Autonomous Driving,” in 2018 IEEE Intelligent Vehicles Symposium (IV), Suzhou, China, 2018, pp. 179–184.
• [19] A. Ćorović, V. Ilić, S. Ðurić, M. Marijan, B. Pavković, “The Real-Time Detection of Traffic Participants Using YOLO Algorithm,” in 2018 26th Telecommunications Forum (TELFOR), Belgrade, Serbia, 2018, pp. 1–4.
• [20] G. Ozturk, R. Koker, O. Eldogan, D. Karayel, “Recognition of Vehicles, Pedestrians and Traffic Signs Using Convolutional Neural Networks,” in 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Istanbul, Turkey, 2020, pp. 1–8.
• [21] N. Kemsaram, A. Das, G. Dubbelman, “An Integrated Framework for Autonomous Driving: Object Detection, Lane Detection, and Free Space Detection,” in 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4), London, UK, 2019, pp. 260–265.
• [22] G. Jocher, K. Nishimura, T. Mineeva, R. Vilariño. YOLOv5 Code Repository. June, 2020. [Online]. Available: https://github.com/ultralytics/yolov5