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Towards Generalized UAV Object Detection: A Novel Perspective from Frequency Domain Disentanglement

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Abstract

When deploying unmanned aerial vehicle (UAV) object detection networks to complex, real-world scenes, generalization ability is often reduced due to domain shift. While most existing domain-generalized object detection methods disentangle domain-invariant features spatially, our exploratory experiments revealed a key insight for UAV object detection (UAV-OD): frequency domain contributions exhibit more pronounced disparities in generalization compared to generic object detection involving larger objects, since UAV-OD detects smaller objects. Therefore, frequency domain disentanglement stands out as a more direct, effective approach for UAV-OD. This paper proposes a novel frequency domain disentanglement method to improve UAV-OD generalization. Specifically, our framework leverages two learnable filters extracting domain-invariant and domain-specific spectra. Additionally, we design two contrastive losses: an image-level loss and an instance-level loss guiding training. These losses enable the filters to focus on extracting domain-invariant and domain-specific spectra, achieving better disentangling. Extensive experiments across multiple datasets, including UAVDT and Visdrone2019-DET, utilizing Faster R-CNN and YOLOv5, show our approach consistently and significantly outperforms baseline and state-of-the-art domain generalization methods. Our code is available at https://github.com/wangkunyu241/UAV-Frequency.

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References

  • Cao, J., Cholakkal, H., Anwer, R.M., Khan, F. S., Pang, Y., & Shao, L. (2020). D2Det: Towards high quality object detection and instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11485–11494).

  • Cao, S., Joshi, D., Gui, L. Y., & Wang, Y. X. (2023). Contrastive mean teacher for domain adaptive object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 23839–23848).

  • Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European conference on computer vision (pp. 213–229). Springer.

  • Carlucci, F. M., D’Innocente, A., Bucci, S., Caputo, B., & Tommasi, T. (2019). Domain generalization by solving jigsaw puzzles. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2229–2238).

  • Chen, C., Li, J., Zhou, H. Y., Han, X., Huang, Y., Ding, X., & Yu, Y. (2022a). Relation matters: Foreground-aware graph-based relational reasoning for domain adaptive object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3677–3694.

  • Chen, C., Zhang, Y., Lv, Q., Wei, S., Wang, X., Sun, X., & Dong, J. (2019). RRNet: A hybrid detector for object detection in drone-captured images. In Proceedings of the IEEE/CVF international conference on computer vision workshops.

  • Chen, C., Zheng, Z., Ding, X., Huang, Y., & Dou, Q. (2020). Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8869–8878).

  • Chen, M., Chen, W., Yang, S., Song, J., Wang, X., Zhang, L., Yan, Y., Qi, D., Zhuang, Y., Xie, D., et al. (2022b). Learning domain adaptive object detection with probabilistic teacher. arXiv preprint arXiv:2206.06293

  • Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018). Domain adaptive faster R-CNN for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3339–3348).

  • Chen, Z., Wang, Y., Yang, Y., & Liu, D. (2021). PSD: Principled synthetic-to-real dehazing guided by physical priors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7180–7189).

  • Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., Han, J. (2023). Towards large-scale small object detection: Survey and benchmarks. IEEE Transactions on Pattern Analysis and Machine Intelligence.

  • Chi, L., Jiang, B., & Mu, Y. (2020). Fast Fourier convolution. Advances in Neural Information Processing Systems, 33, 4479–4488.

    Google Scholar 

  • Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213–3223).

  • Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., & Yang, M. H. (2020). Multi-scale boosted dehazing network with dense feature fusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2157–2167).

  • Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., & Tian, Q. (2018). The unmanned aerial vehicle benchmark: Object detection and tracking. In Proceedings of the European conference on computer vision (ECCV) (pp. 370–386).

  • Duarte, A., Borralho, N., Cabral, P., & Caetano, M. (2022). Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review. Forests, 13(6), 911.

    Article  Google Scholar 

  • Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In International conference on machine learning, PMLR (pp. 1180–1189).

  • Geraldes, R., Goncalves, A., Lai, T., Villerabel, M., Deng, W., Salta, A., Nakayama, K., Matsuo, Y., & Prendinger, H. (2019). UAV-based situational awareness system using deep learning. IEEE Access, 7, 122583–122594.

    Article  Google Scholar 

  • Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 1440–1448).

  • Guo, C., Li, C., Guo, J., Loy, C. C., Hou, J., Kwong, S., & Cong, R. (2020). Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1780–1789).

  • He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9729–9738).

  • 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).

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).

  • Hsu, C.C., Tsai, Y.H., Lin, Y.Y., &Yang, M.H. (2020a). Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In Computer Vision–ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16 (pp. 733–748). Springer.

  • Hsu, H. K., Yao, C. H., Tsai, Y. H., Hung, W. C., Tseng, H. Y., Singh, M., & Yang, M. H. (2020b). Progressive domain adaptation for object detection. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 749–757).

  • Huang, J., Guan, D., Xiao, A., Lu, S. (2021). FSDR: Frequency space domain randomization for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6891–6902).

  • Huang, L., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2019). Iterative normalization: Beyond standardization towards efficient whitening. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4874–4883).

  • Huang, Z., Wang, H., Xing, E. P., & Huang, D. (2020). Self-challenging improves cross-domain generalization. In Computer Vision–ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 (pp. 124–140). Springer.

  • Jeon, S., Hong, K., Lee, P., Lee, J., & Byun, H. (2021). Feature stylization and domain-aware contrastive learning for domain generalization. In Proceedings of the 29th ACM international conference on multimedia (pp. 22–31).

  • Jiang, J., Chen, B., Wang, J., & Long, M. (2021a). Decoupled adaptation for cross-domain object detection. arXiv preprint arXiv:2110.02578

  • Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021b). Enlightengan: Deep light enhancement without paired supervision. IEEE Transactions on Image Processing, 30, 2340–2349.

  • Jocher, G., Changyu, L., Hogan, A., Yu, L., Rai, P., Sullivan, T., et al. (2020). ultralytics/yolov5: Initial release. Zenodo

  • Kajiura, N., Liu, H., & Satoh, S. (2021). Improving camouflaged object detection with the uncertainty of pseudo-edge labels. In ACM multimedia Asia (pp. 1–7).

  • Kiefer, B., Ott, D., & Zell, A. (2022). Leveraging synthetic data in object detection on unmanned aerial vehicles. In 2022 26th international conference on pattern recognition (ICPR) (pp. 3564–3571). IEEE.

  • Lee, S., Bae, J., & Kim, H.Y. (2023). Decompose, adjust, compose: Effective normalization by playing with frequency for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11776–11785).

  • Li, C., Guo, C., & Loy, C. C. (2021). Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8), 4225–4238.

    Google Scholar 

  • Li, D., Huang, J.B., Li, Y., Wang, S., & Yang, M. H. (2016). Weakly supervised object localization with progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3512–3520).

  • Li, W., Liu, X., Yao, X., & Yuan, Y. (2022a). Scan: Cross domain object detection with semantic conditioned adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 1421–1428.

  • Li, W., Liu, X., Yuan, Y. (2022b). Sigma: Semantic-complete graph matching for domain adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5291–5300).

  • Li, Y. J., Dai, X., Ma, C. Y., Liu, Y. C., Chen, K., Wu, B., He, Z., Kitani, K., & Vajda, P. (2022c). Cross-domain adaptive teacher for object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7581–7590).

  • Lin, C., Yuan, Z., Zhao, S., Sun, P., Wang, C., & Cai, J. (2021). Domain-invariant disentangled network for generalizable object detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 8771–8780).

  • Lin, S., Zhang, Z., Huang, Z., Lu, Y., Lan, C., Chu, P., You, Q., Wang, J., Liu, Z., Parulkar, A., et al. (2023). Deep frequency filtering for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11797–11807).

  • Liu, H., Song, P., & Ding, R. (2020a). Towards domain generalization in underwater object detection. In 2020 IEEE international conference on image processing (ICIP) (pp. 1971–1975). IEEE.

  • 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.

    Article  Google Scholar 

  • Liu, Q., Chen, C., Qin, J., Dou, Q., Heng, P. A. (2021). FedDG: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1013–1023).

  • Liu, X., Ma, Y., Shi, Z., & Chen, J. (2019). Griddehazenet: Attention-based multi-scale network for image dehazing. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 7314–7323).

  • Liu, Y., Wang, J., Huang, C., Wang, Y., & Xu, Y. (2023). CIGAR: Cross-modality graph reasoning for domain adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 23776–23786).

  • Lu, Y., Zhong, Z., & Shu, Y. (2023). Multi-view domain adaptive object detection on camera networks. In AAAI.

  • Lygouras, E., Santavas, N., Taitzoglou, A., Tarchanidis, K., Mitropoulos, A., & Gasteratos, A. (2019). Unsupervised human detection with an embedded vision system on a fully autonomous UAV for search and rescue operations. Sensors, 19(16), 3542.

    Article  Google Scholar 

  • Ma, L., Ma, T., Liu, R., Fan, X., & Luo, Z. (2022). Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5637–5646).

  • Mittal, P., Singh, R., & Sharma, A. (2020). Deep learning-based object detection in low-altitude UAV datasets: A survey. Image and Vision Computing, 104, 104046.

    Article  Google Scholar 

  • Nussbaumer, H. J., & Nussbaumer, H. J. (1982). The fast Fourier transform. Springer.

  • Pan, X., Luo, P., Shi, J., & Tang, X. (2018). Two at once: Enhancing learning and generalization capacities via ibn-net. In Proceedings of the European conference on computer vision (ECCV) (pp. 464–479).

  • Pan, X., Zhan, X., Shi, J., Tang, X., & Luo, P. (2019). Switchable whitening for deep representation learning. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1863–1871).

  • Qin, X., Wang, Z., Bai, Y., Xie, X., & Jia, H. (2020). FFA-Net: Feature fusion attention network for single image dehazing. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11908–11915.

    Article  Google Scholar 

  • Saito, K., Ushiku, Y., Harada, T., & Saenko, K. (2019). Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6956–6965).

  • Sakaridis, C., Dai, D., & Van Gool, L. (2018). Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126, 973–992.

    Article  Google Scholar 

  • San, K. T., Mun, S. J., Choe, Y. H., & Chang, Y. S. (2018). UAV delivery monitoring system. In MATEC web of conferences, EDP Sciences (Vol. 151, p. 04011).

  • Song, Y., He, Z., Qian, H., & Du, X. (2023). Vision transformers for single image dehazing. IEEE Transactions on Image Processing, 32, 1927–1941.

    Article  Google Scholar 

  • Sun, K., Liu, H., Ye, Q., Gao, Y., Liu, J., Shao, L., & Ji, R. (2021a). Domain general face forgery detection by learning to weight. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 2638–2646.

  • Sun, W., Dai, L., Zhang, X., Chang, P., & He, X. (2021b). RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring. Applied Intelligence 1–16.

  • Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7167–7176).

  • Vidit, V., Engilberge, M., & Salzmann, M. (2023). Clip the gap: A single domain generalization approach for object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3219–3229).

  • Wang, K., Fu, X., Huang, Y., Cao, C., Shi, G., Zha, Z. J. (2023a). Generalized uav object detection via frequency domain disentanglement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1064–1073).

  • Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B., & Lu, T. (2023b). Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 2654–2662.

  • Wang, Y., Wan, R., Yang, W., Li, H., Chau, L. P., & Kot, A. (2022). Low-light image enhancement with normalizing flow. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2604–2612.

    Article  Google Scholar 

  • Wu, A., & Deng, C. (2022). Single-domain generalized object detection in urban scene via cyclic-disentangled self-distillation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 847–856).

  • Wu, A., Liu, R., Han, Y., Zhu, L., & Yang. Y. (2021a). Vector-decomposed disentanglement for domain-invariant object detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9342–9351).

  • Wu, X., Li, W., Hong, D., Tao, R., & Du, Q. (2021). Deep learning for unmanned aerial vehicle-based object detection and tracking: A survey. IEEE Geoscience and Remote Sensing Magazine, 10(1), 91–124.

    Article  Google Scholar 

  • Wu, Z., Suresh, K., Narayanan, P., Xu, H., Kwon, H., & Wang, Z. (2019). Delving into robust object detection from unmanned aerial vehicles: A deep nuisance disentanglement approach. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1201–1210).

  • Xu, C. D., Zhao, X. R., Jin, X., & Wei, X. S. (2020). Exploring categorical regularization for domain adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11724–11733).

  • Xu, M., Qin, L., Chen, W., Pu, S., & Zhang, L. (2023). Multi-view adversarial discriminator: Mine the non-causal factors for object detection in unseen domains. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8103–8112).

  • Xu, Q., Zhang, R., Zhang, Y., Wang, Y., Tian, Q. (2021). A Fourier-based framework for domain generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 14383–14392).

  • Yang, Q., Niu, H., Xia, P., Zhang, W., & Li, B. (2023). Frequency decomposition to tap the potential of single domain for generalization. arXiv preprint arXiv:2304.07261

  • Yang, Y., & Soatto, S. (2020). FDA: Fourier domain adaptation for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4085–4095).

  • Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2020). BDD100k: A diverse driving dataset for heterogeneous multitask learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2636–2645).

  • Yu, W., Yang, T., & Chen, C. (2021). Towards resolving the challenge of long-tail distribution in UAV images for object detection. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 3258–3267).

  • Zhang, P., Zhong, Y., & Li, X. (2019). Slimyolov3: Narrower, faster and better for real-time UAV applications. In Proceedings of the IEEE/CVF international conference on computer vision workshops.

  • Zhang, X., Cui, P., Xu, R., Zhou, L., He, Y., & Shen, Z. (2021). Deep stable learning for out-of-distribution generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5372–5382).

  • Zhang, X., Xu, Z., Xu, R., Liu, J., Cui, P., Wan, W., Sun, C., & Li, C. (2022). Towards domain generalization in object detection. arXiv preprint arXiv:2203.14387

  • Zhao, L., & Wang, L. (2022). Task-specific inconsistency alignment for domain adaptive object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 14217–14226).

  • Zhao, Y., Zhong, Z., Zhao, N., Sebe, N., & Lee, G. H. (2023). Style-hallucinated dual consistency learning: A unified framework for visual domain generalization. International Journal of Computer Vision.

  • Zheng, Y., Huang, D., Liu, S., & Wang, Y. (2020). Cross-domain object detection through coarse-to-fine feature adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13766–13775).

  • Zheng, Y., Zhan, J., He, S., Dong, J., & Du, Y. (2023). Curricular contrastive regularization for physics-aware single image dehazing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5785–5794).

  • Zhong, Z., Zhao, Y., Lee, G. H., & Sebe, N. (2022). Adversarial style augmentation for domain generalized urban-scene segmentation. Advances in Neural Information Processing Systems, 35, 338–350.

    Google Scholar 

  • Zhou, Z., Li, H., Liu, H., Wang, N., Yu, G., & Ji, R. (2023). Star loss: Reducing semantic ambiguity in facial landmark detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 15475–15484).

  • Zhu, P., Du, D., Wen, L., Bian, X., Ling, H., Hu, Q., Peng, T., Zheng, J., Wang, X., Zhang, Y., et al. (2019). Visdrone-vid2019: The vision meets drone object detection in video challenge results. In Proceedings of the IEEE/CVF international conference on computer vision workshops.

  • Zhuang, C., Han, X., Huang, W., & Scott, M. (2020). iFAN: Image-instance full alignment networks for adaptive object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 13122–13129.

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) under Grants 62225207 and 62276243.

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  1. School of Information Science and Technology and MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, 230026, China

    Kunyu Wang, Xueyang Fu, Chengjie Ge, Chengzhi Cao & Zheng-Jun Zha

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  1. Kunyu Wang
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  3. Chengjie Ge
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Wang, K., Fu, X., Ge, C. et al. Towards Generalized UAV Object Detection: A Novel Perspective from Frequency Domain Disentanglement. Int J Comput Vis 132, 5410–5438 (2024). https://doi.org/10.1007/s11263-024-02108-5

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