Comparison of YOLOv8 Models for Aircraft Detection in Airport Apron Using Digital Image Processing
DOI:
https://doi.org/10.55003/ETH.410309Keywords:
Automated aircraft detection, YOLOv8, Performance, F1-scoreAbstract
Airport safety can be improved by efficient air traffic management, which monitors aircraft parking spots and controls their movement into them more efficiently. In addition to emergency situations, it can assist in accurately inspecting airport areas. Management costs can be reduced by reducing the workload of personnel in controlling and helping manage air and ground traffic. This research focuses on comparing the performance of various YOLOv8 models (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8nl, and YOLOv8nx) in an automated aircraft detection system using digital image processing techniques. The methodology involves collecting a dataset of 1,000 airport apron images with parked aircraft, dividing them into 900 training images and 100 testing images. The YOLOv8 models are trained on the training dataset, and their performance is evaluated on the testing dataset using confusion matrices. Experimental results reveal that YOLOv8nx achieves the highest average aircraft detection performance, with a precision of 0.94, recall of 0.74, and f1-score of 0.83. Additionally, YOLOv8n demonstrates the highest processing speed at 0.95 milliseconds. Consequently, YOLOv8n is suitable for applications requiring high-speed processing, while YOLOv8nx is ideal for tasks demanding the utmost performance efficiency.
References
C. Ye, J. Tian and T. Tian, “Multi Scale SAR Aircraft Detection Based on Swin Transformer and Adaptive Feature Fusion Network,” in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 7058–7061, doi: 10.1109/IGARSS52108.2023.10283111.
Z. Ye, X. Xiao and H. Wang, “Convolutional modulated scattering feature network for aircraft classification in SAR images,” in 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, Jul. 7–12, 2024, doi: 10.1109/IGARSS53475.2024.10641325.
S. Al Mansoori, A. Kunhu and A. AlHammadi, “Effective Airplane Detection in High Resolution Satellite Images using YOLOv3 Model,” in Proc. 2021 4th International Conference on Signal Processing and Information Security (ICSPIS), Nov. 24–25, 2021, pp. 57–60, doi: 10.1109/ICSPIS53734.2021.9652416.
W. Li, S. Xiang, H. Wang and C. Pan, “Robust airplane detection in satellite images,” in 2011 18th IEEE International Conference on Image Processing (ICIP), Sep. 11–14, 2011, pp. 2821–2824, doi: 10.1109/ICIP.2011.6116259.
K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 27–30, 2016, pp. 770–778, doi: 10.1109/CVPR.2016.90.
S. Ren, K. He, R. Girshick and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017, doi: 10.1109/TPAMI.2016.2577031.
K. He, G. Gkioxari, P. Dollár and R. Girshick, “Mask R-CNN,” in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Oct. 22–29, 2017, pp. 2980–2988, doi: 10.1109/ICCV.2017.322.
X. Mou, X. Chen, J. Guan, B. Chen and Y. Dong, “Marine Target Detection Based on Improved Faster R-CNN for Navigation Radar PPI Images,” in 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), Oct. 23–26, 2019, pp. 1–5, doi: 10.1109/ICCAIS46528.2019.9074588.
Y. Qian, Q. Liu, H. Zhu, H. Fan, B. Du and S. Liu, “Mask R-CNN for object detection in multitemporal SAR images,” in 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Aug. 5–7, 2019, pp. 1–4, doi: 10.1109/Multi-Temp.2019.8866949.
J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 27–30, 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.
P. Khumeechai, “Detecting Human Faces in Three Different Environmental Conditions using YOLOv4,” Thai Science and Technology Journal., vol. 32, no. 2, pp. 99–114, 2024, doi: 10.14456/tstj.2024.16.
P. Kumeechai, “Real-time vehicle detection system on the highway,” Journal of Applied Research on Science and Technology (JARST), vol. 22, no. 3, 2023, Art. no. 254011, doi: 10.60101/jarst.2023.254011.
M. Jindal, N. Raj, P. Saranya and S. V, “Aircraft Detection from Remote Sensing Images using YOLOv5 Architecture,” in 2022 6th International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India, Apr. 21–22, 2022, pp. 332–336, doi: 10.1109/ICDCS54290.2022.9780777.
A. Afdhal, K. Saddami, S. Sugiarto, Z. Fuadi and N. Nasaruddin, “Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment,” in 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), Banda Aceh, Indonesia, Aug. 2–3, 2023, pp. 260–265, doi: 10.1109/COSITE60233.2023.10249521.
S. S. Sriram, V. Parisha, B. U. K. Varma and C. Srinivasulu, “Traffic Light Detection and Recognition using YOLOv8,” in 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Jun. 5–7, 2024, pp. 1227–1233, doi: 10.1109/ICAAIC60222.2024.10575768.
R. Bawankule, V. Gaikwad, I. Kulkarni, S. Kulkarni, A. Jadhav and N. Ranjan, “Visual Detection of Waste using YOLOv8,” in 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, Jun. 14–16, 2023, pp. 869–873, doi: 10.1109/ICSCSS57650.2023.10169688.
Y. Lu, L. Zhang and W. Xie, “YOLO-compact: An Efficient YOLO Network for Single Category Real-Time Object Detection,” in 2020 Chinese Control And Decision Conference (CCDC), Hefei, China, Aug. 22–24, 2020, pp. 1931–1936, doi: 10.1109/CCDC49329.2020.9164580.
A Anish, R Sharan, A. Malini and T. Archana, “Enhancing Surveillance Systems with YOLO Algorithm for Real-Time Object Detection and Tracking,” in 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, Dec. 11–13, pp. 1254–1257, doi: 10.1109/ICACRS58579.2023.10404710.
Q. B. Phan and T. T. Nguyen, “A Novel Approach for PV Cell Fault Detection Using YOLOv8 and Particle Swarm Optimization,” in 2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS), Tempe, AZ, USA, Aug. 6–9, 2023, pp. 634–638, doi: 10.1109/MWSCAS57524.2023.10406139.
C. You and H. Kong, “Improved Steel Surface Defect Detection Algorithm Based on YOLOv8,” IEEE Access, vol. 12, pp. 99570–99577, 2024, doi: 10.1109/ACCESS.2024.3429555.
D. B. Walther, “Using Confusion Matrices to Estimate Mutual Information between Two Categorical Measurements,” in 2013 International Workshop on Pattern Recognition in Neuroimaging, Philadelphia, PA, USA, Jun. 22–24, 2013, pp. 220–224, 10.1109/PRNI.2013.63.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 School of Engineering, King Mongkut’s Institute of Technology Ladkrabang
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The published articles are copyrighted by the School of Engineering, King Mongkut's Institute of Technology Ladkrabang.
The statements contained in each article in this academic journal are the personal opinions of each author and are not related to King Mongkut's Institute of Technology Ladkrabang and other faculty members in the institute.
Responsibility for all elements of each article belongs to each author; If there are any mistakes, each author is solely responsible for his own articles.
