YOLOv5-based Vehicle Objects Detection Using UAV Images

Yıl 2022, Cilt: 06 Sayı: 1, 40 - 45, 31.08.2022

Zeynep Nur Duman , Müzeyyen Büşra Çulcu , Oğuzhan Katar

https://doi.org/10.34110/forecasting.1145381

Öz

Traffic is the situation and movement of pedestrians, animals, and vehicles on highways. The regulation of these movements and situations is also a basic problem of traffic engineering. It is necessary to collect data about traffic in order to produce suitable solutions to problems by traffic engineers. Traffic data can be collected with equipment such as cameras and sensors. However, these data need to be analysed in order to transform them into meaningful information. For a difficult task such as calculating and optimizing traffic density, traffic engineers need information on the number of vehicles to be obtained from the image data they have collected. In this process, artificial intelligence-based computer systems can help researchers. This study proposes a deep learning-based system to detect vehicle objects using YOLOv5 model. A public dataset containing 15,474 high-resolution UAV images was used in the training of the model. Dataset samples were cropped to 640×640px sub-images, and sub-images that did not contain vehicle objects were filtered out. The filtered dataset samples were divided into 70% training, 20% validation, and 10% testing. The YOLOv5 model reached 99.66% precision, 99.44% recall, 99.66% mAP@0.5, and 89.35% mAP@0.5-0.95% during the training phase. When the determinations made by the model on the images reserved for the test phase are examined, it is seen that it has achieved quite successful results. By using the proposed approach in daily life, the detection of vehicle objects from high-resolution images can be automated with high success rates.

Anahtar Kelimeler

Object detection, Vehicle, YOLOv5, Deep learning, UAV

Kaynakça

• G. Mattioli, C. Roberts, J.K. Steinberger, A. Brown, The political economy of car dependence: A systems of provision approach, Energy Research and Social Science. 66 (2020). doi:10.1016/j.erss.2020.101486.
• C. Buchanan, Traffic in Towns: A study of the long term problems of traffic in urban areas, Routledge. (2015)
• Z. Sun, G. Bebis, R. Miller, On-road vehicle detection: A review, IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 (2006) 694–711. doi:10.1109/TPAMI.2006.104.
• E.V. Butilă, R.G. Boboc, Urban Traffic Monitoring and Analysis Using Unmanned Aerial Vehicles (UAVs): A Systematic Literature Review, Remote Sensing. 14 (2022). doi:10.3390/rs14030620.
• R. Shrestha, R. Bajracharya, S. Kim, 6G Enabled Unmanned Aerial Vehicle Traffic Management: A Perspective, IEEE Access. 9 (2021) 91119–91136. doi:10.1109/ACCESS.2021.3092039.
• Y. Akbari, N. Almaadeed, S. Al-maadeed, O. Elharrouss, Applications, databases and open computer vision research from drone videos and images: a survey, Artificial Intelligence Review. 54 (2021) 3887–3938. doi:10.1007/s10462-020-09943-1.
• X. Li, F. Men, S. Lv, X. Jiang, M. Pan, Q. Ma, H. Yu, Vehicle detection in very-high-resolution remote sensing images based on an anchor-free detection model with a more precise foveal area, ISPRS International Journal of Geo-Information. 10 (2021). doi:10.3390/ijgi10080549
• G. Fragapane, R. de Koster, F. Sgarbossa, J.O. Strandhagen, Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda, European Journal of Operational Research. 294 (2021) 405–426. doi:10.1016/j.ejor.2021.01.019.
• B. Liu, C. Han, X. Liu, W. Li, Vehicle Artificial Intelligence System Based on Intelligent Image Analysis and 5G Network, International Journal of Wireless Information Networks. (2021). doi:10.1007/s10776-021-00535-6.
• S. Ghaffarian, J. Valente, M. van der Voort, B. Tekinerdogan, Effect of attention mechanism in deep learning-based remote sensing image processing: A systematic literature review, Remote Sensing. 13 (2021). doi:10.3390/rs131529
• Z. Chen, L. Cao, Q. Wang, YOLOv5-Based Vehicle Detection Method for High-Resolution UAV Images, Mobile Information Systems. 2022 (2022). doi:10.1155/2022/1828848.
• J. Sang, Z. Wu, P. Guo, H. Hu, H. Xiang, Q. Zhang, B. Cai, An improved YOLOv2 for vehicle detection, Sensors (Switzerland). 18 (2018). doi:10.3390/s18124272.
• H. Song, H. Liang, H. Li, Z. Dai, X. Yun, Vision-based vehicle detection and counting system using deep learning in highway scenes, European Transport Research Review. 11 (2019). doi:10.1186/s12544-019-0390-4.
• E. Puertas, G. De-Las-heras, J. Fernández-Andrés, J. Sánchez-Soriano, Dataset: Roundabout Aerial Images for Vehicle Detection, Data (Basel). 7 (2022). doi:10.3390/data7040047.
• J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2016: pp. 779–788. doi:10.1109/CVPR.2016.91.