Comparison of YOLO Versions for Object Detection from Aerial Images

Year 2022, Volume: 9 Issue: 2, 87 - 93, 02.06.2022

Muhammed Enes Atik , Zaide Duran , Roni Özgünlük

https://doi.org/10.30897/ijegeo.1010741

Cited By: 7

Abstract

Many different disciplines use deep Learning algorithms for various purposes. In recent years, object detection by deep learning from aerial or terrestrial images has become a popular research area. In this study, object detection application was performed by training the YOLOv2 and YOLOv3 algorithms in the Google Colaboratory cloud service with the help of Python software language with the DOTA dataset consisting of aerial photographs. 43 aerial photographs containing 9 class objects were used for evaluation. Accuracy analyzes of these two algorithms were made according to Recall, Precision and F-score for 9 classes, and the results were compared accordingly. YOLOv2 gave better results in 5 out of 9 classes, while YOLOv3 gave better results in recognizing small objects. While YOLOv2 can detect objects in an average photograph in 43 seconds, YOLOv3 has achieved superior performance in terms of time by detecting objects in an average of 2.5 seconds.

Keywords

Computer Vision, Deep Learning, Object Detection, YOLO, Aerial Image

References

• Cepni, S., Atik, M. E., & Duran, Z. (2020). Vehicle detection using different deep learning algorithms from image sequence. Baltic Journal of Modern Computing, 8(2), 347-358.
• Atik, M. E., & Duran, Z. (2020, October). Deep Learning-Based 3D Face Recognition Using Derived Features from Point Cloud. In The Proceedings of the Third International Conference on Smart City Applications (pp. 797-808). Springer, Cham.
• Atik, S. O., & Ipbuker, C. (2021). Ship Detection from Satellite Images with Instance Segmentation (Uydu Görüntülerinden Örnek Segmentasyonu ile Gemi Tespiti). 18. Harita Bilimsel ve Teknik Kurultayı, 29-29 Mayıs 2021, Ankara.
• Atik, S. O., & Ipbuker, C. (2020). Instance Segmentation Of Crowd Detection In The Camera Images. In Proceeding of Asian Conference on Remote Sensing 2020 (ACRS 2020).
• Yang, M. Y., Liao, W., Li, X., Cao, Y., & Rosenhahn, B. (2019). Vehicle Detection in Aerial Images. Photogrammetric engineering and remote sensing: PE&RS, 85(4), 297-304.
• Chen, E., Gong, Y., Tie, Y. (2016). Advances in Multimedia Information Processing. Category Aggregation Among Region Proposals for Object Detection. China: 17th Pasific Rim Conference on Multimedia Xi’an, 210-211.
• He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
• Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 91-99.
• Dai, J., Li, Y., He, K., & Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. In Advances in neural information processing systems (pp. 379-387).
• Gavrilova, M., Chang, J., Thalmann N. M., Hitzer, E., Ishikawa, H. (2019). Advances in Computer Graphics. Object Perception in the RGB Image. Canada: 36th Computer Graphics International Conference, 478-430.
• Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).
• Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
• Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014, September). Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.
• Lu, J., Sibai, H., Fabry, E., Forsyth, D. (2017). NO need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles. USA: University of Illinois. arXiv preprint arXiv: 1707.03501v1.
• Shafiee, M. J., Chywl, B., Li, F., Wong, A. (2017). Fast YOLO: A Fast You Only Look Once System for Real-Time Embedded Object Detection in Video. Canada: University of Waterloo. preprint arXiv: 1709.05943v1.
• Tan, L., Dong, X., Ma, Y., Yu, C. (2018). A Multiple Object Tracking Algorithm Based on YOLO Detection. In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI 2018). China: Beijing Technology and Business University.
• Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Canada: University of Toronto.
• He, S., Lau, R. W. H., Liu, W., Huang, Z., Yang, Q. (2015). SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection. International Journal of Computer Vision. doi 10.1007/s11263-015-0822-0.
• Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J. (2017). Light-Head R-CNN: In Defense of Two-Stage Object Detector. China: Tsinghua University. preprint arXiv: 1711.07264v2 45
• Liu, M., Wang, X., Zhou, A., Fu, X., Ma, Y., & Piao, C. (2020). UAV-YOLO: small object detection on unmanned aerial vehicle perspective. Sensors, 20(8), 2238.
• Xia, G. S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., ... & Zhang, L. (2018). DOTA: A large-scale dataset for object detection in aerial images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3974-3983).
• Ding, J., Xue, N., Long, Y., Xia, G. S., & Lu, Q. (2018). Learning RoI transformer for detecting oriented objects in aerial images. arXiv preprint arXiv:1812.00155.
• Ding, J., Xue, N., Xia, G. S., Bai, X., Yang, W., Yang, M. Y., ... & Zhang, L. (2021). Object detection in aerial images: A large-scale benchmark and challenges. arXiv preprint arXiv:2102.12219.
• Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). IEEE.
• Atik, S. O., & Ipbuker, C. (2021). Integrating Convolutional Neural Network and Multiresolution Segmentation for Land Cover and Land Use Mapping Using Satellite Imagery. Applied Sciences, 11(12), 5551.
• Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
• 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.
• Zhao, L., & Li, S. (2020). Object detection algorithm based on improved YOLOv3. Electronics, 9(3), 537.
• Gonultas, F., Atik, M. E., & Duran, Z. (2020). Extraction of roof planes from different point clouds using RANSAC algorithm. International Journal of Environment and Geoinformatics, 7(2), 165-171.
• Atik, M. E., Duran, Z., & Seker, D. Z. (2021). Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features. ISPRS International Journal of Geo-Information, 10(3), 187.