Araştırma Makalesi
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Yıl 2022, Cilt 9, Sayı 2, 87 - 93, 02.06.2022
https://doi.org/10.30897/ijegeo.1010741

Öz

Kaynakça

  • 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.

Comparison of YOLO Versions for Object Detection from Aerial Images

Yıl 2022, Cilt 9, Sayı 2, 87 - 93, 02.06.2022
https://doi.org/10.30897/ijegeo.1010741

Öz

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.

Kaynakça

  • 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.

Ayrıntılar

Birincil Dil İngilizce
Konular Uzaktan Algılama
Bölüm Research Articles
Yazarlar

Muhammed Enes ATİK> (Sorumlu Yazar)
İSTANBUL TEKNİK ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ, FOTOGRAMETRİ (DR)
0000-0003-2273-7751
Türkiye


Zaide DURAN>
İSTANBUL TEKNİK ÜNİVERSİTESİ
0000-0002-1608-0119
Türkiye


Roni ÖZGÜNLÜK Bu kişi benim
ISTANBUL TECHNICAL UNIVERSITY
0000-0003-4772-5992
Türkiye

Yayımlanma Tarihi 2 Haziran 2022
Yayınlandığı Sayı Yıl 2022, Cilt 9, Sayı 2

Kaynak Göster

Bibtex @araştırma makalesi { ijegeo1010741, journal = {International Journal of Environment and Geoinformatics}, eissn = {2148-9173}, address = {}, publisher = {Cem GAZİOĞLU}, year = {2022}, volume = {9}, number = {2}, pages = {87 - 93}, doi = {10.30897/ijegeo.1010741}, title = {Comparison of YOLO Versions for Object Detection from Aerial Images}, key = {cite}, author = {Atik, Muhammed Enes and Duran, Zaide and Özgünlük, Roni} }
APA Atik, M. E. , Duran, Z. & Özgünlük, R. (2022). Comparison of YOLO Versions for Object Detection from Aerial Images . International Journal of Environment and Geoinformatics , 9 (2) , 87-93 . DOI: 10.30897/ijegeo.1010741
MLA Atik, M. E. , Duran, Z. , Özgünlük, R. "Comparison of YOLO Versions for Object Detection from Aerial Images" . International Journal of Environment and Geoinformatics 9 (2022 ): 87-93 <https://dergipark.org.tr/tr/pub/ijegeo/issue/66005/1010741>
Chicago Atik, M. E. , Duran, Z. , Özgünlük, R. "Comparison of YOLO Versions for Object Detection from Aerial Images". International Journal of Environment and Geoinformatics 9 (2022 ): 87-93
RIS TY - JOUR T1 - Comparison of YOLO Versions for Object Detection from Aerial Images AU - Muhammed EnesAtik, ZaideDuran, RoniÖzgünlük Y1 - 2022 PY - 2022 N1 - doi: 10.30897/ijegeo.1010741 DO - 10.30897/ijegeo.1010741 T2 - International Journal of Environment and Geoinformatics JF - Journal JO - JOR SP - 87 EP - 93 VL - 9 IS - 2 SN - -2148-9173 M3 - doi: 10.30897/ijegeo.1010741 UR - https://doi.org/10.30897/ijegeo.1010741 Y2 - 2021 ER -
EndNote %0 International Journal of Environment and Geoinformatics Comparison of YOLO Versions for Object Detection from Aerial Images %A Muhammed Enes Atik , Zaide Duran , Roni Özgünlük %T Comparison of YOLO Versions for Object Detection from Aerial Images %D 2022 %J International Journal of Environment and Geoinformatics %P -2148-9173 %V 9 %N 2 %R doi: 10.30897/ijegeo.1010741 %U 10.30897/ijegeo.1010741
ISNAD Atik, Muhammed Enes , Duran, Zaide , Özgünlük, Roni . "Comparison of YOLO Versions for Object Detection from Aerial Images". International Journal of Environment and Geoinformatics 9 / 2 (Haziran 2022): 87-93 . https://doi.org/10.30897/ijegeo.1010741
AMA Atik M. E. , Duran Z. , Özgünlük R. Comparison of YOLO Versions for Object Detection from Aerial Images. International Journal of Environment and Geoinformatics. 2022; 9(2): 87-93.
Vancouver Atik M. E. , Duran Z. , Özgünlük R. Comparison of YOLO Versions for Object Detection from Aerial Images. International Journal of Environment and Geoinformatics. 2022; 9(2): 87-93.
IEEE M. E. Atik , Z. Duran ve R. Özgünlük , "Comparison of YOLO Versions for Object Detection from Aerial Images", International Journal of Environment and Geoinformatics, c. 9, sayı. 2, ss. 87-93, Haz. 2022, doi:10.30897/ijegeo.1010741