Research Article
BibTex RIS Cite

Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights with YOLO Architecture

Year 2024, Volume: 20 Issue: 2, 28 - 36, 28.06.2024

Abdil Karakan

https://doi.org/10.18466/cbayarfbe.1432356

Abstract

In the study, red, yellow, and green lights at traffic lights were detected in real-world conditions and in real time. To adapt to real-world conditions, A data set was prepared from traffic lights in different locations, lighting conditions, and angles. A total of 5273 photographs of different traffic lights and different burning lamps were used in the data set. Additionally, grayscale, bevel, blur, variability, added noise, changed image brightness, changed color vibrancy, changed perspective, and resized and changed position have been added to photos. With these additions, the error that may occur due to any distortion from the camera is minimized. Four different YOLO architectures were used to achieve the highest accuracy rate on the dataset. As a result, the study obtained the highest accuracy at 98.3% in the YOLOV8 architecture, with an F1-Score of 0.939 and mAP@.5 value of 0.977. Since the work will be done in real time, the number of frames per second (FPS) must be the highest. The highest FPS number was 60 in the YOLOv8 architecture.

Keywords

Deep learning traffic light real-time detection YOLOV8

References

  • [1]. Diaz-Cabrera, M, Cerri, P, Medici, P. 2015. Robust real-time traffic light detection and distance estimation using a single camera. Expert. Syst. Appl; 42, 3911–3923. https://doi.org/10.1016/j.eswa.2014.12.037
  • [2]. Hosseinyalamdary, S, Yilmaz, A. 2017. A Bayesian approach to traffic light detection and mapping, ISPRS J. Photograms. Remote Sens; 125, 184–192. https://doi.org/10.1016/j.isprsjprs.2017.01.008
  • [3]. Li, X, Ma, H, Wang, X, Zhang, X. 2018. Traffic light recognition for complex scene with fusion detections. IEEE Trans. Intell. Transp. Syst; 19, 199–208. https://doi.org/10.1109/TITS.2017.2749971
  • [4]. Boloor, A, Garimella, K, He, K, Gill, C, Vorobeychik, Y, Zhang, X. 2020. Attacking vi- sion-based perception in end-to-end autonomous driving models. J. Syst. Archit; 101766. . https://doi.org/10.1016/j.sysarc.2020.101766
  • [5]. Jensen, M.P, Philipsen, M.P, Møgelmose, A, Moeslund, T.B, Trivedi, M.M. 2016. Vision for looking at traffic lights: issues, survey, and perspectives. IEEE Trans. Intell. Transp. Syst; 17, 7, 1800–1815. https://doi.org/10.1109/TITS.2015.2509509
  • [6]. Ouyang, Z, Niu, J, Liu, Y, Guizani, M. 2019. Deep cnn-based real-time traffic light detector for self-driving vehicles. IEEE Trans. Mob. Comput; 19, 2, 300–313. https://doi.org/10.1109/TMC.2019.2892451
  • [7]. Kim, J, Cho, H, Hwangbo, M, Choi, J, Canny, J, Kwon, Y.P. Deep traffic light detection for self-driving cars from a large-scale dataset, in: 2018. 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, 4-7 November 2018, pp. 280–285. https://doi.org/10.1109/ITSC.2018.8569575
  • [8]. Jensen, M, Philipsen, M, Møgelmose, A, Moeslund, T, Trivedi, M. 2016. Vision for looking at traffic lights: issues, survey, and perspectives. IEEE Trans. ITS; 17, 7, 1800–1815. https://doi.org/10.1109/TITS.2015.2509509
  • [9]. Behrendt, K, Novak, L, Botros, R. 2017. A deep learning approach to traffic lights: detection, tracking, and classification. IEEE ICRA; pp. 1370–1377. https://doi.org/10.1109/ICRA.2017.7989163
  • [10]. Sommer, L.W, Schuchert, T, Beyerer, J. Fast deep vehicle detection in aerial images. IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE; Santa Rose, California, USA. 24-31 March 2017. pp. 311–319. https://doi.org/10.1109/WACV.2017.41.
  • [11]. Zhang, X, Story, B, Rajan, D. 2021. Night time vehicle detection and tracking by fusing vehicle parts from multiple cameras. IEEE Transa. Intelligent Transp. Syst; pp. 258-265. http://dx.doi.org/10.1109/TITS.2021.3076406
  • [12]. Chen, C, Liu, B, Wan, S, Qiao, P, Pei, Q. 2021. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans. Intelligent Trans. Syst; 22, 3, 1840–1852. https://doi.org/10.1155/2021/5583874
  • [13]. Zhang, Z, Zaman, A, Xu, J, Liu, X. 2022. Artificial intelligence-aided railroad trespassing detection and data analytics: methodology and a case study. Accident Anal. Prevention; 168, 106594. https://doi.org/10.1016/j.aap.2022.106594
  • [14]. Tsai, L.W, Hsieh, J.W, Fan, K.C. 2007. Vehicle detection using normalized color and edge map. IEEE Trans. Image Process; 16, 3, 850–864. http://dx.doi.org/10.1109/tip.2007.891147
  • [15]. Ehtesham, H, Yasser, K, Imtiaz, A. 2023. Learning deep feature fusion for traffic light detection. Journal of Engineering Research; 11, 94–99. http://dx.doi.org/10.1155/2020/7286187
  • [16]. Zhenchao, O, Jianwei, N, Tao, R, Yanqi, L, Jiahe, C, Jiyan, W. 2020. MBBNet: An edge IoT computing-based traffic light detection solution for autonomous bus. Journal of Systems Architecture; 109, 101835. http://dx.doi.org/10.1016/j.sysarc.2020.101835
  • [17]. Jean, P, V, M, Lucas, T, Rodrigo, F, B, Thiago, M, Alberto, F, S, Claudine, B, Nicu, S, Thiago, O, S. 2021. Deep traffic light detection by overlaying synthetic context on arbitrary natural images. Computers & Graphics; 94, 76–86. https://doi.org/10.48550/arXiv.2011.03841
  • [18]. Moises, D.C, Pietro, C, Paolo, M. 2015. Robust real-time traffic light detection and distance estimation using a single camera. Expert Systems with Applications; 42, 3911–3923. https://doi.org/10.1016/j.eswa.2014.12.037
  • [19]. Eunseop, L, Daijin, L. 2019 Accurate traffic light detection using deep neural network with focal regression loss. Image and Vision Computing, 87, 24–36. https://doi.org/10.1016/j.imavis.2019.04.003
  • [20]. Feng, G, Yi, W, Yu, Q. 2023. Real-time dense traffic detection using lightweight backbone and improved path aggregation feature pyramid network. Journal of Industrial Information Integration; 31, 100427. https://doi.org/10.1016/j.jii.2022.100427
  • [21]. Chuanxi, N, Kexin, L. 2022. Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet. Appl. Sci; 12, 10808. https://doi.org/10.3390/app122110808
  • [22]. Lin, T,Y, Goyal, P, Girshick, R, He, K. Doll´ar, P. Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision, Venice, Italy, 22-29 Oct. 2017, pp. 2980–2988. https://doi.org/10.1109/ICCV.2017.324
  • [23]. Liu, Y, Sun, P, Wergeles, N, Shang, Y. 2021. A survey and performance evaluation of deep learning methods for small object detection. Expert Syst. Applied, 172, 114602. https://doi.org/10.1016/j.eswa.2021.114602
  • [24]. Yu, W, Yang, T, Chen, C.Towards resolving the challenge of long-tail distribution in uav images for object detection, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; Waikoloa, HI, USA. 3-8 June 2021. pp. 3258–3267. https://doi.org/10.48550/arXiv.2011.03822
  • [25]. Sang, J, Wu, Z, Guo, P, Hu, H, Xiang, H, Zhang, Q, Cai, B. 2018. An improved YOLOv2 for vehicle detection. Sensors; 18, 12, 4272. https://doi.org/10.3390/s18124272
  • [26]. Zhang, F, Yang, F, Li, C, Yuan, G. 2019. CMNet: a connect-and-merge convolutional neural network for fast vehicle detection in urban traffic surveillance. IEEE Access; 7, 72660–72671. https://doi.org/10.1155/2021/5583874
  • [27]. Tan, M, Pang, R, Le, and Q.W. Efficientdet: scalable and efficient object detection. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; Seattle, Washington, USA. 14-19 June 2020. pp. 10781–10790. https://doi.org/10.1109/CVPR42600.2020.01079
  • [28]. Xu, R, Lin, H, Lu, K, Cao, L, Liu, Y. 2021. A Forest Fire Detection System Based on Ensemble Learning. MDPI Forests, 12 (217), pp. 1568–1570. https://doi.org/10.3390/f12020217
  • [29]. Xie, X, He, C. Object detection of armored vehicles based on deep learning in battlefield environment. Proceedings - 2017 4th International Conference on Information Science and Control Engineering, ICISCE; Changsha, Chania, 21-23 July 2017. 1568–1570. https://doi.org/10.1109/ICISCE.2017.327
  • [30]. Boyuk, M, Duvar, R, Urhan, O. Deep learning based vehicle detection with images taken from unmanned air vehicle. Proceedings 2020 Innovations in Intelligent Systems and Applications Conference, ASYU; İstanbul, Türkiye, 15-17 October 2020. pp.175. https://doi.org/10.1109/ASYU50717.2020.9259868
  • [31]. Kamran, F, Shahzad, M, Shafait, F. Automated military vehicle detection from low-altitude aerial images. 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA. Canberra, Australis, 10-13 December 2018, 2 https://doi.org/10.1109/DICTA.2018.8615865
  • [32]. Gupta, P, Pareek, B, Singal, G, Rao, D. V. 2022. Edge device based military vehicle detection and classification from UAV. Multimedia Tools and Applications; 81(14), 19813–19834. https://doi.org/10.1007/S11042-021-11242-Y/FIGURES/12
  • [33]. Kyrkou, C, Plastiras, G, Theocharides, T, Venieris, S. I, Bouganis, C. S. 2018. DroNet: Efficient convolutional neural network detector for real-time UAV applications. Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition; 967–972. https://doi.org/10.23919/DATE.2018.8342149
  • [34]. Sun, Y, Wang, W, Zhang, Q, Ni, H, Zhang, X. 2022. Improved YOLOv5 with transformer for large scene military vehicle detection on SAR image. 2022 7th International Conference on Image, Vision and Computing, ICIVC; 87–93. https://doi.org/10.1109/ICIVC55077.2022.9887095
  • [35]. Yong, S. P, Yeong, Y. C. 2018. Human object detection in forest with deep learning based on drone’s vision. 2018 4th International Conference on Computer and Information Sciences: Revolutionizing Digital Landscape for Sustainable Smart Society, ICCOINS; https://doi.org/10.1109/ICCOINS.2018.8510564
  • [36]. Mansour, A, Hassan, A, Hussein, W. M, Said, E. 2019. Automated vehicle detection in satellite images using deep learning. IOP Conference Series: Materials Science and Engineering; 610(1). https://doi.org/10.1088/1757-899X/610/1/012027
  • [37]. Bayram, A.F, Nabiyev, V. 2023. Detection of Hidden Camouflaged Tanks Based on Deep Learning: Comparative Analysis of the art YOLO Network. Gümüşhane University of Journal of Science and Technology; 182-193. https://doi.org/10.17714/gumusfenbil.1271208
  • [38]. Gelayol, G, Ignacio, M.A, Qi, W, Jose, M.A.C. 2023. Robust Real-Time Traffic Light Detection on Small-Form Platform for Autonomous Vehicles. Journal of Intelligent Transportation System; 1-11 https://doi.org/10.1080/15472450.2023.2205018
  • [39]. Hassan, E, Khalil, Y, Ahmad, I. 2023. Learning Deep Feature Fusion for Traffic Light Detection. Journal of Engineering Research; 11, 94-99. https://doi.org/10.1016/j.jer.2023.100128
  • [40]. Ngoc, H.T, Nguyen, K.H, Hua, H.K, Nguyen. H.V.N, Quach, L. 2023. Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2. International Journal of Advanced Computer Science and Applications; Vol.14. 7. https://doi.org/10.14569/IJACSA.2023.0140752
  • [41]. Gao, H, Wang, W, Yang, C, Jiao, W, Chen, Z, Zhang, T. 2021. Traffic Signal Image Detection Technology Based on YOLO. Journal of Physics: Conference Series; 1-21. https://doi.org/10.1088/1742-6596/1961/1/012012
  • [42]. Serhan, N.H, Olmary, A. Y. 2022. Traffic Light Detection Using Opencv and YOLO. International Conference on Innovation and Intelligence for Informatics, Computing and Technologies; 604-608. https://doi.org/ 10.1088/1742-6596/1961/1/012012
  • [43]. Omar, W, Lee, I., Lee, G, Park, K.M. 2020. Detection and Localization of Traffic Light Using YOLOv3 and Stereo Vision. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; Vol.18, 1247-1252. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1247-2020
  • [44]. Nui, C, Li, K. 2022. Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet, Applied Science; 12, 10808. https://doi.org/10.3390/app122110808

Year 2024, Volume: 20 Issue: 2, 28 - 36, 28.06.2024

Abdil Karakan

https://doi.org/10.18466/cbayarfbe.1432356

Abstract

References

  • [1]. Diaz-Cabrera, M, Cerri, P, Medici, P. 2015. Robust real-time traffic light detection and distance estimation using a single camera. Expert. Syst. Appl; 42, 3911–3923. https://doi.org/10.1016/j.eswa.2014.12.037
  • [2]. Hosseinyalamdary, S, Yilmaz, A. 2017. A Bayesian approach to traffic light detection and mapping, ISPRS J. Photograms. Remote Sens; 125, 184–192. https://doi.org/10.1016/j.isprsjprs.2017.01.008
  • [3]. Li, X, Ma, H, Wang, X, Zhang, X. 2018. Traffic light recognition for complex scene with fusion detections. IEEE Trans. Intell. Transp. Syst; 19, 199–208. https://doi.org/10.1109/TITS.2017.2749971
  • [4]. Boloor, A, Garimella, K, He, K, Gill, C, Vorobeychik, Y, Zhang, X. 2020. Attacking vi- sion-based perception in end-to-end autonomous driving models. J. Syst. Archit; 101766. . https://doi.org/10.1016/j.sysarc.2020.101766
  • [5]. Jensen, M.P, Philipsen, M.P, Møgelmose, A, Moeslund, T.B, Trivedi, M.M. 2016. Vision for looking at traffic lights: issues, survey, and perspectives. IEEE Trans. Intell. Transp. Syst; 17, 7, 1800–1815. https://doi.org/10.1109/TITS.2015.2509509
  • [6]. Ouyang, Z, Niu, J, Liu, Y, Guizani, M. 2019. Deep cnn-based real-time traffic light detector for self-driving vehicles. IEEE Trans. Mob. Comput; 19, 2, 300–313. https://doi.org/10.1109/TMC.2019.2892451
  • [7]. Kim, J, Cho, H, Hwangbo, M, Choi, J, Canny, J, Kwon, Y.P. Deep traffic light detection for self-driving cars from a large-scale dataset, in: 2018. 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, 4-7 November 2018, pp. 280–285. https://doi.org/10.1109/ITSC.2018.8569575
  • [8]. Jensen, M, Philipsen, M, Møgelmose, A, Moeslund, T, Trivedi, M. 2016. Vision for looking at traffic lights: issues, survey, and perspectives. IEEE Trans. ITS; 17, 7, 1800–1815. https://doi.org/10.1109/TITS.2015.2509509
  • [9]. Behrendt, K, Novak, L, Botros, R. 2017. A deep learning approach to traffic lights: detection, tracking, and classification. IEEE ICRA; pp. 1370–1377. https://doi.org/10.1109/ICRA.2017.7989163
  • [10]. Sommer, L.W, Schuchert, T, Beyerer, J. Fast deep vehicle detection in aerial images. IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE; Santa Rose, California, USA. 24-31 March 2017. pp. 311–319. https://doi.org/10.1109/WACV.2017.41.
  • [11]. Zhang, X, Story, B, Rajan, D. 2021. Night time vehicle detection and tracking by fusing vehicle parts from multiple cameras. IEEE Transa. Intelligent Transp. Syst; pp. 258-265. http://dx.doi.org/10.1109/TITS.2021.3076406
  • [12]. Chen, C, Liu, B, Wan, S, Qiao, P, Pei, Q. 2021. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans. Intelligent Trans. Syst; 22, 3, 1840–1852. https://doi.org/10.1155/2021/5583874
  • [13]. Zhang, Z, Zaman, A, Xu, J, Liu, X. 2022. Artificial intelligence-aided railroad trespassing detection and data analytics: methodology and a case study. Accident Anal. Prevention; 168, 106594. https://doi.org/10.1016/j.aap.2022.106594
  • [14]. Tsai, L.W, Hsieh, J.W, Fan, K.C. 2007. Vehicle detection using normalized color and edge map. IEEE Trans. Image Process; 16, 3, 850–864. http://dx.doi.org/10.1109/tip.2007.891147
  • [15]. Ehtesham, H, Yasser, K, Imtiaz, A. 2023. Learning deep feature fusion for traffic light detection. Journal of Engineering Research; 11, 94–99. http://dx.doi.org/10.1155/2020/7286187
  • [16]. Zhenchao, O, Jianwei, N, Tao, R, Yanqi, L, Jiahe, C, Jiyan, W. 2020. MBBNet: An edge IoT computing-based traffic light detection solution for autonomous bus. Journal of Systems Architecture; 109, 101835. http://dx.doi.org/10.1016/j.sysarc.2020.101835
  • [17]. Jean, P, V, M, Lucas, T, Rodrigo, F, B, Thiago, M, Alberto, F, S, Claudine, B, Nicu, S, Thiago, O, S. 2021. Deep traffic light detection by overlaying synthetic context on arbitrary natural images. Computers & Graphics; 94, 76–86. https://doi.org/10.48550/arXiv.2011.03841
  • [18]. Moises, D.C, Pietro, C, Paolo, M. 2015. Robust real-time traffic light detection and distance estimation using a single camera. Expert Systems with Applications; 42, 3911–3923. https://doi.org/10.1016/j.eswa.2014.12.037
  • [19]. Eunseop, L, Daijin, L. 2019 Accurate traffic light detection using deep neural network with focal regression loss. Image and Vision Computing, 87, 24–36. https://doi.org/10.1016/j.imavis.2019.04.003
  • [20]. Feng, G, Yi, W, Yu, Q. 2023. Real-time dense traffic detection using lightweight backbone and improved path aggregation feature pyramid network. Journal of Industrial Information Integration; 31, 100427. https://doi.org/10.1016/j.jii.2022.100427
  • [21]. Chuanxi, N, Kexin, L. 2022. Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet. Appl. Sci; 12, 10808. https://doi.org/10.3390/app122110808
  • [22]. Lin, T,Y, Goyal, P, Girshick, R, He, K. Doll´ar, P. Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision, Venice, Italy, 22-29 Oct. 2017, pp. 2980–2988. https://doi.org/10.1109/ICCV.2017.324
  • [23]. Liu, Y, Sun, P, Wergeles, N, Shang, Y. 2021. A survey and performance evaluation of deep learning methods for small object detection. Expert Syst. Applied, 172, 114602. https://doi.org/10.1016/j.eswa.2021.114602
  • [24]. Yu, W, Yang, T, Chen, C.Towards resolving the challenge of long-tail distribution in uav images for object detection, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; Waikoloa, HI, USA. 3-8 June 2021. pp. 3258–3267. https://doi.org/10.48550/arXiv.2011.03822
  • [25]. Sang, J, Wu, Z, Guo, P, Hu, H, Xiang, H, Zhang, Q, Cai, B. 2018. An improved YOLOv2 for vehicle detection. Sensors; 18, 12, 4272. https://doi.org/10.3390/s18124272
  • [26]. Zhang, F, Yang, F, Li, C, Yuan, G. 2019. CMNet: a connect-and-merge convolutional neural network for fast vehicle detection in urban traffic surveillance. IEEE Access; 7, 72660–72671. https://doi.org/10.1155/2021/5583874
  • [27]. Tan, M, Pang, R, Le, and Q.W. Efficientdet: scalable and efficient object detection. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; Seattle, Washington, USA. 14-19 June 2020. pp. 10781–10790. https://doi.org/10.1109/CVPR42600.2020.01079
  • [28]. Xu, R, Lin, H, Lu, K, Cao, L, Liu, Y. 2021. A Forest Fire Detection System Based on Ensemble Learning. MDPI Forests, 12 (217), pp. 1568–1570. https://doi.org/10.3390/f12020217
  • [29]. Xie, X, He, C. Object detection of armored vehicles based on deep learning in battlefield environment. Proceedings - 2017 4th International Conference on Information Science and Control Engineering, ICISCE; Changsha, Chania, 21-23 July 2017. 1568–1570. https://doi.org/10.1109/ICISCE.2017.327
  • [30]. Boyuk, M, Duvar, R, Urhan, O. Deep learning based vehicle detection with images taken from unmanned air vehicle. Proceedings 2020 Innovations in Intelligent Systems and Applications Conference, ASYU; İstanbul, Türkiye, 15-17 October 2020. pp.175. https://doi.org/10.1109/ASYU50717.2020.9259868
  • [31]. Kamran, F, Shahzad, M, Shafait, F. Automated military vehicle detection from low-altitude aerial images. 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA. Canberra, Australis, 10-13 December 2018, 2 https://doi.org/10.1109/DICTA.2018.8615865
  • [32]. Gupta, P, Pareek, B, Singal, G, Rao, D. V. 2022. Edge device based military vehicle detection and classification from UAV. Multimedia Tools and Applications; 81(14), 19813–19834. https://doi.org/10.1007/S11042-021-11242-Y/FIGURES/12
  • [33]. Kyrkou, C, Plastiras, G, Theocharides, T, Venieris, S. I, Bouganis, C. S. 2018. DroNet: Efficient convolutional neural network detector for real-time UAV applications. Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition; 967–972. https://doi.org/10.23919/DATE.2018.8342149
  • [34]. Sun, Y, Wang, W, Zhang, Q, Ni, H, Zhang, X. 2022. Improved YOLOv5 with transformer for large scene military vehicle detection on SAR image. 2022 7th International Conference on Image, Vision and Computing, ICIVC; 87–93. https://doi.org/10.1109/ICIVC55077.2022.9887095
  • [35]. Yong, S. P, Yeong, Y. C. 2018. Human object detection in forest with deep learning based on drone’s vision. 2018 4th International Conference on Computer and Information Sciences: Revolutionizing Digital Landscape for Sustainable Smart Society, ICCOINS; https://doi.org/10.1109/ICCOINS.2018.8510564
  • [36]. Mansour, A, Hassan, A, Hussein, W. M, Said, E. 2019. Automated vehicle detection in satellite images using deep learning. IOP Conference Series: Materials Science and Engineering; 610(1). https://doi.org/10.1088/1757-899X/610/1/012027
  • [37]. Bayram, A.F, Nabiyev, V. 2023. Detection of Hidden Camouflaged Tanks Based on Deep Learning: Comparative Analysis of the art YOLO Network. Gümüşhane University of Journal of Science and Technology; 182-193. https://doi.org/10.17714/gumusfenbil.1271208
  • [38]. Gelayol, G, Ignacio, M.A, Qi, W, Jose, M.A.C. 2023. Robust Real-Time Traffic Light Detection on Small-Form Platform for Autonomous Vehicles. Journal of Intelligent Transportation System; 1-11 https://doi.org/10.1080/15472450.2023.2205018
  • [39]. Hassan, E, Khalil, Y, Ahmad, I. 2023. Learning Deep Feature Fusion for Traffic Light Detection. Journal of Engineering Research; 11, 94-99. https://doi.org/10.1016/j.jer.2023.100128
  • [40]. Ngoc, H.T, Nguyen, K.H, Hua, H.K, Nguyen. H.V.N, Quach, L. 2023. Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2. International Journal of Advanced Computer Science and Applications; Vol.14. 7. https://doi.org/10.14569/IJACSA.2023.0140752
  • [41]. Gao, H, Wang, W, Yang, C, Jiao, W, Chen, Z, Zhang, T. 2021. Traffic Signal Image Detection Technology Based on YOLO. Journal of Physics: Conference Series; 1-21. https://doi.org/10.1088/1742-6596/1961/1/012012
  • [42]. Serhan, N.H, Olmary, A. Y. 2022. Traffic Light Detection Using Opencv and YOLO. International Conference on Innovation and Intelligence for Informatics, Computing and Technologies; 604-608. https://doi.org/ 10.1088/1742-6596/1961/1/012012
  • [43]. Omar, W, Lee, I., Lee, G, Park, K.M. 2020. Detection and Localization of Traffic Light Using YOLOv3 and Stereo Vision. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; Vol.18, 1247-1252. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1247-2020
  • [44]. Nui, C, Li, K. 2022. Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet, Applied Science; 12, 10808. https://doi.org/10.3390/app122110808
There are 44 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Articles
Authors

Abdil Karakan 0000-0003-1651-7568

Publication Date June 28, 2024
Submission Date February 5, 2024
Acceptance Date May 2, 2024
Published in Issue Year 2024 Volume: 20 Issue: 2

Cite

APA Karakan, A. (2024). Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights with YOLO Architecture. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 20(2), 28-36. https://doi.org/10.18466/cbayarfbe.1432356
AMA Karakan A. Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights with YOLO Architecture. CBUJOS. June 2024;20(2):28-36. doi:10.18466/cbayarfbe.1432356
Chicago Karakan, Abdil. “Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights With YOLO Architecture”. Celal Bayar Üniversitesi Fen Bilimleri Dergisi 20, no. 2 (June 2024): 28-36. https://doi.org/10.18466/cbayarfbe.1432356.
EndNote Karakan A (June 1, 2024) Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights with YOLO Architecture. Celal Bayar Üniversitesi Fen Bilimleri Dergisi 20 2 28–36.
IEEE A. Karakan, “Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights with YOLO Architecture”, CBUJOS, vol. 20, no. 2, pp. 28–36, 2024, doi: 10.18466/cbayarfbe.1432356.
ISNAD Karakan, Abdil. “Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights With YOLO Architecture”. Celal Bayar Üniversitesi Fen Bilimleri Dergisi 20/2 (June 2024), 28-36. https://doi.org/10.18466/cbayarfbe.1432356.
JAMA Karakan A. Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights with YOLO Architecture. CBUJOS. 2024;20:28–36.
MLA Karakan, Abdil. “Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights With YOLO Architecture”. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, vol. 20, no. 2, 2024, pp. 28-36, doi:10.18466/cbayarfbe.1432356.
Vancouver Karakan A. Detection of Red, Yellow, and Green Lights in Real-Time Traffic Lights with YOLO Architecture. CBUJOS. 2024;20(2):28-36.
 Download Cover Image