Member: Ersin KILIÇ (E-mail)
An accurate car counting in aerial images based on convolutional neural networks ,
Ersin Kilic, Serkan Öztürk
SOTA of CARPK(SOTA of CARPK)
This project aims to implement a simple and effective single-shot detector model to detect and count the cars in aerial images.
Experiments on the CARPK car dataset have shown the state-of-the-art counting and localizing performance of the proposed method compared with existing methods.
| CARPK Dataset | MAE | RMSE |
| LPN [1] | 23.80 | 36.79 |
| RetinaNet[2] | 16.62 | 22.30 |
| YOLOv3[3] | 7.92 | 11.08 |
| IoUNet[4] | 6.77 | 8.52 |
| VGG-GAP-HR[5] | 7.88 | 9.30 |
| GSP224[6] | 5.46 | 8.09 |
| SA+CF+CRT[7] | 5.42 | 7.38 |
| GANet (VGG-16)[8] | 5.80 | 6.90 |
| Ours (VGG-16) | 2.12 | 3.02 |
| Original | Heat map | Peak map |
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2- Start visdom and browse (http://localhost:8097/)
3- Run test.py
[1] M. Hsieh, Y. Lin, W. H. Hsu, Drone-based object counting by spatially regularized regional proposal network, CoRR abs/1707.05972. arXiv: 1707.05972.
[2] T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar, Focal loss for dense object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence (2018) 1–1doi:10.1109/tpami.2018.2858826.
[3] J. Redmon, A. Farhadi, Yolov3: An incremental improvement, CoRR abs/1804.02767. arXiv:1804.02767.
[4] E. Goldman, R. Herzig, A. Eisenschtat, O. Ratzon, I. Levi, J. Goldberger, T. Hassner, Precise detection in densely packed scenes, CoRR abs/1904.00853. arXiv:1904.00853.
[5] S. Aich, I. Stavness, Improving object counting with heatmap regulation, CoRR abs/1803.05494. arXiv:1803.05494.
[6] S. Aich, I. Stavness, Object counting with small datasets of large images, CoRR abs/1805.11123. arXiv:1805.11123.
[7] W. Li, H. Li, Q. Wu, X. Chen, K. N. Ngan, Simultaneously detecting and counting dense vehicles from drone images, IEEE Transactions on Industrial Electronics 66 (12) (2019) 9651–9662. doi:10.1109/tie.2019.2899548.
[8] Y. Cai, D. Du, L. Zhang, L. Wen, W. Wang, Y. Wu, S. Lyu, Guided attention network for object detection and counting on drones, CoRR abs/1909.11307. arXiv:1909.11307.
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