Abstract
As a branch of object detection task, the detection of queuing people in the aerial images emerges as a new research direction in recent years. However, it remains challenging due to the complex background, variations in the viewpoints and densely located tiny objects. In this paper, we propose a tiny object detector called QPDet to solve the above issues. Firstly, a large-scale dataset of queuing people with dense and diverse annotations has been constructed to enrich the knowledge of this field, in which a large number of queuing people have been sorted and collected using drones. Secondly, a modified YOLOX has been utilized as our detector, which effectively detects the tiny objects in the aerial images. Finally, an adaptive soft label assignment strategy has been proposed to improve the robustness of our detector. Extensive experiments have been conducted to demonstrate the effectiveness of our approach, the results exhibit the superiority of our detector.
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References
Cheng, G., et al.: Towards large-scale small object detection: survey and benchmarks. IEEE Trans. Pattern Anal. Mach. Intell. (2023)
Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., Cho, K.: Augmentation for small object detection. arXiv preprint arXiv:1902.07296 (2019)
Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q.: Vision meets drones: a challenge. arXiv preprint arXiv:1804.07437 (2018)
Xu, C., Wang, J., Yang, W., Yu, L.: Dot distance for tiny object detection in aerial images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1192–1201 (2021)
Yu, X., Gong, Y., Jiang, N., Ye, Q., Han, Z.: Scale match for tiny person detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1257–1265 (2020)
Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3221 (2017)
Guo, C., Fan, B., Zhang, Q., Xiang, S., Pan, C.: Augfpn: improving multi-scale feature learning for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12595–12604 (2020)
Li, Y., Chen, Y., Wang, N., Zhang, Z.: Scale-aware trident networks for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6054–6063 (2019)
Bai, Y., Zhang, Y., Ding, M., Ghanem, B.: Sod-mtgan: small object detection via multi-task generative adversarial network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 206–221 (2018)
Singh, B., Najibi, M., Davis, L.S.: Sniper: efficient multi-scale training. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Yang, C., Huang, Z., Wang, N.: Querydet: cascaded sparse query for accelerating high-resolution small object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13668–13677 (2022)
Bell, S., Zitnick, C.L., Bala, K., Girshick, R.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2874–2883 (2016)
Xu, C., Wang, J., Yang, W., Yu, H., Yu, L., Xia, G.-S.: RFLA: Gaussian receptive field based label assignment for tiny object detection. In: European Conference on Computer Vision, pp. 526–543. Springer, Cham (2022)
Zhang, S., et al.: Dense distinct query for end-to-end object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7329–7338 (2023)
Huang, X., Ge, Z., Jie, Z., Yoshie, O.: NMS by representative region: towards crowded pedestrian detection by proposal pairing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10750–10759 (2020)
Chi, C., Zhang, S., Xing, J., Lei, Z., Li, S.Z., Zou, X.: Pedhunter: occlusion robust pedestrian detector in crowded scenes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 10639–10646 (2020)
Oyelade, O.N., Ezugwu, A.E.-S.: A state-of-the-art survey on deep learning methods for detection of architectural distortion from digital mammography. IEEE Access 8, 148644–148676 (2020)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Bochkovskiy, A.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Jiang, Y., Tan, Z., Wang, J., Sun, X., Lin, M., Li, H.: Giraffedet: a heavy-neck paradigm for object detection. arXiv preprint arXiv:2202.04256 (2022)
Tan, M., Pang, R., Le, Q.V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)
Li, Z., Hou, B., Zitong, W., Ren, B., Yang, C.: FCOSR: a simple anchor-free rotated detector for aerial object detection. Remote Sens. 15(23), 5499 (2023)
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Ge, Z., Liu, S., Li, Z., Yoshie, O., Sun, J.: OTA: optimal transport assignment for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 303–312 (2021)
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Kingma, D.P.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Xu, S., et al. Pp-yoloe: an evolved version of yolo. arXiv preprint arXiv:2203.16250 (2022)
Sun, Y., Cao, B., Zhu, P., Qinghua, H.: Drone-based RGB-infrared cross-modality vehicle detection via uncertainty-aware learning. IEEE Trans. Circuits Syst. Video Technol. 32(10), 6700–6713 (2022)
Acknowledgment
This work is supported by the Intelligent Policing Key Laboratory of Sichuan Province, No. ZNJW2024FKMS004.
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Zhang, Y., Su, Y., Li, S., Yi, K. (2025). QPDet: Queuing People Detector for Aerial Images Based on Adaptive Soft Label Assignment Strategy. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15305. Springer, Cham. https://doi.org/10.1007/978-3-031-78169-8_2
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