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Research Articles

Improved TPH for object detection in aerial images

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Pages 493-505 | Received 07 Jun 2023, Accepted 06 Aug 2023, Published online: 04 Sep 2023

References

  • Bai, Y., et al., 2018. Sod-mtgan: small object detection via multi-task generative adversarial network. In: Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 206–221.
  • Bejiga, M.B., Zeggada, A., and Melgani, F., 2016. Convolutional neural networks for near real-time object detection from UAV imagery in avalanche search and rescue operations.
  • Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M., 2020. Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. doi:10.48550/arXiv.2004.10934.
  • Deng, S., et al., 2021. A global-local self-adaptive network for drone-view object detection. TIP, 30, 1556–1569.
  • Du, D., et al., 2019. VisDrone-DET2019: the vision meets drone object detection in image challenge results. In: Proceedings of the IEEE/CVF international conference on computer vision workshops, Seoul, South Korea.
  • Everingham, M. and Winn, J., 2011. The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Analysis, Statistical Modelling and Computational Learning, Tech. Rep, 8, 5.
  • Ezequiel, C.A.F., et al., 2014. UAV aerial imaging applications for post-disaster assessment, environmental management and infrastructure development. In: 2014 international conference on unmanned aircraft systems (ICUAS), Orlando, Florida, USA: IEEE, 274–283.
  • Girshick, R., et al., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, USA, 580–587.
  • Girshick, R., 2015. Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, Santiago, Chile, 1440–1448.
  • He, K., et al., 2017. Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, 2961–2969.
  • Huang, Y., Chen, J., and Huang, D., 2022. UFPMP-det: toward accurate and efficient object detection on drone imagery. Proceedings of the AAAI Conference on Artificial Intelligence, 36 (1), 1026–1033. doi:10.1609/aaai.v36i1.19986
  • Jin, R. and Lin, D., 2019. Adaptive anchor for fast object detection in aerial image. IEEE Geoscience and Remote Sensing Letters, 17 (5), 839–843. doi:10.1109/LGRS.2019.2936173
  • Jocher, G., Stoken, A., and Borovec, J., NanoCode012, Ayush Chaurasia, TaoXie, Liu Changyu, Abhiram V, Laughing, tkianai, yxNONG, Adam Hogan, lorenzomammana, AlexWang1900, Jan Hajek, Laurentiu Diaconu, Marc, Yonghye Kwon, oleg, wanghaoyang0106, Yann Defretin, Aditya Lohia, ml5ah, Ben Milanko, Benjamin Fineran, Daniel Khromov, Ding Yiwei, Doug, Durgesh, and Francisco Ingham. ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations, Apr 2021.
  • Kisantal, M., et al., 2019. Augmentation for small object detection. arXiv preprint arXiv:1902.07296. doi:10.5121/csit.2019.91713.
  • Li, C., et al., 2020. Density map guided object detection in aerial images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Seattle, Washington, USA, 190–191.
  • Li, J., et al., 2017. Perceptual generative adversarial networks for small object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu,Hawaii,USA, 1222–1230.
  • Lin, T.Y., et al., 2014. Microsoft coco: Common objects in context[C]//Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. Springer International Publishing, 740–755.
  • Lin, T.-Y., et al., 2017a. Focal loss for dense object detection. ICCV, 3 (7), 8.
  • Lin, T.Y., et al., 2017b. Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu,Hawaii,USA, 2117–2125.
  • Lin, T.Y., et al., 2017c. Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, 2980–2988.
  • Liu, S., et al., 2018. Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 8759–8768
  • Liu, W., et al., 2016. Ssd: single shot multibox detector. In: Computer vision–ECCV 2016: 14th European conference, October 11–14, 2016, Proceedings, Part I 14. Amsterdam, The Netherlands: Springer International Publishing, 21–37.
  • Liu, Y., et al., 2022. NRT-YOLO: improved YOLOv5 based on nested residual transformer for tiny remote sensing object detection. Sensors, 22 (13), 4953. doi:10.3390/s22134953
  • Noh, J., et al., 2019. Better to follow, follow to be better: towards precise supervision of feature super-resolution for small object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, South Korea, 9725–9734.
  • Redmon, J., et al., 2016. You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 779–788.
  • Redmon, J. and Farhadi, A., 2018. Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767. doi:10.48550/arXiv.1804.02767.
  • Ren, S., et al., 2015. Faster r-cnn: towards real-time object detection with region proposal networks, In: Advances in neural information processing systems, 91–99.
  • Shen, Q., Jiang, L., and Xiong, H., Oct 2018. Person tracking and frontal face capture with UAV. In: Proceeding IEEE 18th international conference on communication technology (ICCT), Chongqing, China, 1412–1416.
  • Sunkara, R. and Luo, T., 2022. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Grenoble, France, Cham: Springer Nature Switzerland.
  • Vaswani, A., et al., 2017. Attention is all you need. Advances in Neural Information Processing Systems, 30.
  • Wang, C.-Y., et al., 2020. Cspnet: a new backbone that can enhance learning capability of cnn. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Seattle, USA, 390–391.
  • Wang, J., et al., 2019. Region proposal by guided anchoring. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, Long Beach, USA, 2965–2974.
  • Wei, Z., et al., 2020. AMRNet: chips augmentation in aerial images object detection. arXiv preprint arXiv:2009.07168. doi:10.48550/arXiv.2009.07168.
  • Woo, S., et al., 2018. Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 3–19.
  • Xia, G.S., et al., 2018. DOTA: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, 3974–3983.
  • Yang, F., et al., 2019. Clustered object detection in aerial images. In: Proceedings of the IEEE/CVF international conference on computer vision, Seoul, South Korea, 8311–8320.
  • Zhang, J., et al., 2019. How to fully exploit the abilities of aerial image detectors. In: ICCVW ,Seoul, South Korea.
  • Zhang, S., et al., 2017. S3fd: single shot scale-invariant face detector. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, 192–201.
  • Zhu, X., et al., 2021. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF international conference on computer vision, Montreal, Canada, 2778–2788.

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