Accurate and efficient detection of aircraft at airports is crucial for enhancing airport security, ensuring safe operations, and improving overall air traffic management. As airports grow in complexity and traffic density increases, automated object detection systems powered by advanced algorithms have become essential to support real-time monitoring and decision-making processes. This study presents a comprehensive evaluation of the YOLOv5 object detection algorithm for identifying aircraft at airports, comparing its performance against YOLOv4 and SSD. The goal was to determine the most effective algorithm for real-time detection in complex airport environments. YOLOv5 was trained and tested on a dataset of annotated images of aircraft, and its performance was assessed using precision, recall, mean average precision (mAP), F1 score, and inference speed as key metrics. Results show that YOLOv5 outperforms both YOLOv4 and SSD, achieving the highest precision (0.759), recall (0.772), mAP (0.766), and F1 score (0.765), while maintaining a competitive inference speed of 54 ms. In comparison, SSD demonstrated faster inference speed at 48 ms but lower detection accuracy, while YOLOv4 exhibited the lowest performance across all metrics. These findings indicate that YOLOv5 is the most suitable algorithm for real-time aircraft detection at airports, balancing high detection accuracy with efficient processing speed. This makes it a valuable tool for applications such as airport security and aerial monitoring. 

Keywords - aircraft detection, airport security, aerlail monitoring, YOLO, SSD

[1] Y. Amit, P. Felzenszwalb, R. Girshick, “Object detection,” In: Ikeuchi, K. (eds) Computer Vision. Springer, Cham, 2021. https://doi.org/10.1007/978-3-030-63416-2_660

[2] Z. Liu, Y. Gao, Q. Du, M. Chen, W. Lv, "YOLO-Extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images," IEEE Access, vol. 11, pp. 1742-1751, 2023. https://doi.org/10.1109/ACCESS.2023.3233964

[3] S. Zhou, M. Xie, Y. Jin, F. Miao, C. Ding, "An End-to-end Multi-task Object Detection using Embedded GPU in Autonomous Driving," 2021 22nd International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA, 2021, pp. 122-128. https://doi.org/10.1109/ISQED51717.2021.9424308

[4] M. Bakirci, I. Bayraktar, "Integrating UAV-Based Aerial Monitoring and SSD for Enhanced Traffic Management in Smart Cities," 2024 Mediterranean Smart Cities Conference (MSCC), Martil - Tetuan, Morocco, 2024, pp. 1-6. https://doi.org/10.1109/MSCC62288.2024.10696996

[5] M. Bakirci, I. Bayraktar, "YOLOv9-Enabled Vehicle Detection for Urban Security and Forensics Applications," 2024 12th International Symposium on Digital Forensics and Security (ISDFS), San Antonio, TX, USA, 2024, pp. 1-6. https://doi.org/10.1109/ISDFS60797.2024.10527304

[6] Q. Wu, D. Feng, C. Cao, X. Zeng, Z. Feng, J. Wu, Z. Huang, “Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images,” Sensors, vol. 21, no. 2618, 2021. https://doi.org/10.3390/s21082618

[7] M. Bakirci, I. Bayraktar, "Boosting aircraft monitoring and security through ground surveillance optimization with YOLOv9," 2024 12th International Symposium on Digital Forensics and Security (ISDFS), pp. 1-6, San Antonio, TX, USA, 2024. https://doi.org/10.1109/ISDFS60797.2024.10527349

[8] Y., Wang, T. Wang, X. Zhou, W. Cai, R. Liu, et al., “TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer,” Computational Intelligence and Neuroscience, vol. 2022, no. 2262549, 2022. https://doi.org/10.1155/2022/2262549

[9] J.H. Kim, N. Kim, Y.W. Park, C.S. Won, “Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset,” J. Mar. Sci. Eng., vol. 10, no. 377, 2022. https://doi.org/10.3390/jmse10030377

[10] I.H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions.” SN COMPUT. SCI., vol. 2, no. 160, 2021. https://doi.org/10.1007/s42979-021-00592-x

[11] M.P. Mathew, T.Y. Mahesh, “Leaf-based disease detection in bell pepper plant using YOLO v5,” SIViP 16, pp. 841–847, 2022. https://doi.org/10.1007/s11760-021-02024-y

[12] R. Li, Y. Wu, “Improved YOLO v5 Wheat Ear Detection Algorithm Based on Attention Mechanism,” Electronics, vol. 11, no. 1673, 2022. https://doi.org/10.3390/electronics11111673

[13] O. Hmidani, E.M.I. 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. https://doi.org/10.1109/CommNet56067.2022.9993862

[14] S. Singh, U. Ahuja, M. Kumar, et al., “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment,” Multimedia Tools and Applications, vol. 80, pp. 19753–19768, 2021. https://doi.org/10.1007/s11042-021-10711-8

[15] M. Feroz, M. Sultana, M. Hasan, A. Sarker, P. Chakraborty, T. Choudhury, “Object Detection and Classification from a Real-Time Video Using SSD and YOLO Models,” In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 1349. Springer, Singapore, 2022. https://doi.org/10.1007/978-981-16-2543-5_4

[16] M. Bakirci, "Smart city air quality management through leveraging drones for precision monitoring," Sustainable Cities and Society, vol. 106, no. 105390, 2024. https://doi.org/10.1016/j.scs.2024.105390

[17] M. Bakirci, B. Toptas, "Kinematics and Autoregressive Model Analysis of a Differential Drive Mobile Robot," 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, 2022, pp. 1-6. https://doi.org/10.1109/HORA55278.2022.9800071

[18] M. Bakirci, P. Dmytrovych, I. Bayraktar, O. Anatoliyovych, "Multi-Class Vehicle Detection and Classification with YOLO11 on UAV-Captured Aerial Imagery," 2024 IEEE 7th International Conference on Actual Problems of Unmanned Aerial Vehicles Development (APUAVD), Kyiv, Ukraine, 2024, pp. 191-196. https://doi.org/10.1109/APUAVD64488.2024.10765862

[19] P. Jiang, D. Ergu, F. Liu, Y. Cai, B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Computer Science, vol. 199, pp. 1066-1073, 2022. https://doi.org/10.1016/j.procs.2022.01.135

[20] M. Bakirci, I. Bayraktar, "Refining transportation automation with convolutional neural network-based vehicle detection via UAVs," 2024 International Russian Automation Conference (RusAutoCon), pp. 150-155, Sochi, Russian Federation, 2024. https://doi.org/10.1109/RusAutoCon61949.2024.10694108

[21] M. Zhang, L. Yin, "Solar Cell Surface Defect Detection Based on Improved YOLO v5," IEEE Access, vol. 10, pp. 80804-80815, 2022. https://doi.org/10.1109/ACCESS.2022.3195901

[22] L. Ting, Z. Baijun, Z. Yongsheng, Y. Shun, "Ship Detection Algorithm based on Improved YOLO V5," 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE), Dalian, China, 2021, pp. 483-487. https://doi.org/10.1109/CACRE52464.2021.9501331

[23] F. Zhou, H. Zhao, Z. Nie, "Safety Helmet Detection Based on YOLOv5," 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China, 2021, pp. 6-11. https://doi.org/10.1109/ICPECA51329.2021.9362711

[24] G. Ma, M. Wu, Z. Wu, W. Yang, “Single-shot multibox detector- and building information modeling-based quality inspection model for construction projects,” Journal of Building Engineering, vol. 38, no. 102216, 2021. https://doi.org/10.1016/j.jobe.2021.102216