ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Multi-stage distillation for incremental detection of time-sensitive targets in UAV images
Received date: 2025-03-10
Revised date: 2025-05-23
Accepted date: 2025-06-23
Online published: 2025-07-03
Supported by
Key Research and Development Program of Shaanxi Province(2024CY2-GJHX-42);National Natural Science Foundation of China(62276274);Shaanxi SanqinElite Special Support Program(2024-SQ-001)
To address the problems of catastrophic forgetting， overfitting， and difficulty in adapting dense detector characteristics leading to limited detection accuracy faced by the current time-sensitive target class incremental object detection of UAV images， this paper proposes a time-sensitive target incremental detection algorithm based on multi-stage distillation. The proposed method includes three key modules： the continuous Wasserstein distance-based Inter-Class Distillation （WICD） module， the Prototype-Guided Intra-class Consistency Distillation （PGICD） module， and the Cross-prediction Adaptive Distillation （CAD） module. The WICD module captures the inter-class feature differences from the feature graphs and semantic query vectors using Gaussian distributions with continuous Wasserstein distances to enhance the inter-class discriminability. The PGICD module enhances the inter-class discriminative properties by minimizing the high-level semantic query of the instances in both the teacher’s network and student’s network， and the low-level feature graphs with the prototype differences to achieve effective intra-class feature transfer and enhance intra-class consistency. The CAD module optimizes the cross-prediction distillation process by dynamically adjusting the distillation weights of the classification and regression branches， mitigating the problem of catastrophic forgetting in incremental learning， and improving the model’s detection accuracy in complex scenarios. Experimental results on SIMD and MAR20 datasets show that the proposed method performs well in all types of one-step and multi-step incremental scenarios， and the Average Precision （AP） is significantly improved compared with traditional methods. The AP is as high as 70.8% in the incremental scenario of SIMD dataset with 8+7 classes， with an absolute gap of 1.7% and a relative gap of 2.3% from the upper limit. The results also show that the AP is as high as 60.2% in the incremental scenario of MAR20 dataset with 10+10 classes， with an absolute gap of 2.3% and a relative gap of 3.6% from the upper limit. In addition， the effectiveness of each module is verified by ablation experiments， which effectively improves the incremental detection performance of time-sensitive targets in UAV images.
Zhenhao CHENG , Xiaogang YANG , Ruitao LU , Tao ZHANG , Siyu WANG . Multi-stage distillation for incremental detection of time-sensitive targets in UAV images[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(24) : 331959 -331959 . DOI: 10.7527/S1000-6893.2025.31959
| [1] | ZHANG X R， ZHANG T Y， WANG G C， et al. Remote sensing object detection meets deep learning： A metareview of challenges and advances［J］. IEEE Geoscience and Remote Sensing Magazine， 2023， 11（4）： 8-44. |
| [2] | 赵其昌， 吴一全， 苑玉彬. 光学遥感图像舰船目标检测与识别方法研究进展［J］. 航空学报， 2024， 45（8）： 029025. |
| ZHAO Q C， WU Y Q， YUAN Y B. Progress of ship detection and recognition methods in optical remote sensing images［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（8）： 029025 （in Chinese）. | |
| [3] | 刘延芳， 佘佳宇， 袁秋帆， 等. 无人机遥感图像实时小目标检测方法［J］. 航空学报， 2024， 45（14）： 630119. |
| LIU Y F， SHE J Y， YUAN Q F， et al. Real-time small target detection networks for UAV remote sensing［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（14）： 630119 （in Chinese）. | |
| [4] | 肖欣林， 施伟超， 郑向涛， 等. 基于多模型协同的舰船目标检测［J］. 航空学报， 2024， 45（14）： 630241. |
| XIAO X L， SHI W C， ZHENG X T， et al. Multiple models collaboration for ship detection［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（14）： 630241 （in Chinese）. | |
| [5] | LI Z， WANG Y C， ZHANG N， et al. Deep learning-based object detection techniques for remote sensing images： A survey［J］. Remote Sensing， 2022， 14（10）： 2385. |
| [6] | ZHOU D W， WANG Q W， QI Z H， et al. Class-incremental learning： A survey［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2024， 46（12）： 9851-9873. |
| [7] | MASANA M， LIU X L， TWARDOWSKI B， et al. Class-incremental learning： Survey and performance evaluation on image classification［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2023， 45（5）： 5513-5533. |
| [8] | MA B T， CONG Y， REN Y. IOSL： Incremental open set learning［J］. IEEE Transactions on Circuits and Systems for Video Technology， 2024， 34（4）： 2235-2248. |
| [9] | KANG M X， ZHANG J P， ZHANG J M， et al. Alleviating catastrophic forgetting of incremental object detection via within-class and between-class knowledge distillation［C］∥ 2023 IEEE/CVF International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2023： 18848-18858. |
| [10] | 方维维， 陈爱方， 孟娜， 等. 基于知识蒸馏的目标检测模型增量深度学习方法［J］. 工程科学与技术， 2022， 54（6）： 59-66. |
| FANG W W， CHEN A F， MENG N， et al. Incremental deep learning method for object detection model based on knowledge distillation［J］. Advanced Engineering Sciences， 2022， 54（6）： 59-66 （in Chinese）. | |
| [11] | KIM J， CHO H， KIM J， et al. SDDGR： Stable diffusion-based deep generative replay for class incremental object detection［C］∥ 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2024： 28772-28781. |
| [12] | KIM J， KU Y， KIM J， et al. VLM-PL： Advanced pseudo labeling approach for class incremental object detection via vision-language model［C］∥ 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW）. Piscataway： IEEE Press， 2024： 4170-4181. |
| [13] | PENG C， ZHAO K， LOVELL B C. Faster ILOD： Incremental learning for object detectors based on faster RCNN［J］. Pattern Recognition Letters， 2020， 140： 109-115. |
| [14] | FENG T， WANG M， YUAN H J. Overcoming catastrophic forgetting in incremental object detection via elastic response distillation［C］∥ 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2022： 9417-9426. |
| [15] | LIU Y Y， CONG Y， GOSWAMI D， et al. Augmented box replay： Overcoming foreground shift for incremental object detection［C］∥ 2023 IEEE/CVF International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2023： 11333-11343. |
| [16] | LIU Y Y， SCHIELE B， VEDALDI A， et al. Continual detection transformer for incremental object detection［C］∥ 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2023： 23799-23808. |
| [17] | LI W Z， ZHOU J W， LI X， et al. InfRS： Incremental few-shot object detection in remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2024， 62： 5644314. |
| [18] | 张涛， 杨小冈， 卢孝强， 等. Dense RFB和LSTM遥感图像舰船目标检测［J］. 遥感学报， 2022， 26（9）： 1859-1871. |
| ZHANG T， YANG X G， LU X Q， et al. Ship detection in remote sensing image based on dense RFB and LSTM［J］. National Remote Sensing Bulletin， 2022， 26（9）： 1859-1871 （in Chinese）. | |
| [19] | JOSEPH K J， RAJASEGARAN J， KHAN S， et al. Incremental object detection via meta-learning［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2022， 44（12）： 9209-9216. |
| [20] | BAI L， SONG H， FENG T， et al. Revisiting class-incremental object detection： An efficient approach via intrinsic characteristics alignment and task decoupling［J］. Expert Systems with Applications， 2024， 257： 125057. |
| [21] | MO Q J， GAO Y P， FU S H， et al. Bridge past and future： Overcoming information asymmetry in incremental object detection［C］∥ Computer Vision-ECCV 2024. Cham： Springer Nature Switzerland， 2024： 463-480. |
| [22] | LU X N， DIAO W H， LI J X， et al. Few-shot incremental object detection in aerial imagery via dual-frequency prompt［J］. IEEE Transactions on Geoscience and Remote Sensing， 2024， 62： 5624017. |
| [23] | YU Q Z， ZHU K， WANG W， et al. Incremental object detection with image-level labels［J］. IEEE Transactions on Artificial Intelligence， 2024， 5（5）： 2331-2341. |
| [24] | DONG N， ZHANG Y Q， DING M L， et al. Incremental-DETR： Incremental few-shot object detection via self-supervised learning［J］.Proceedings of the AAAI Conference on Artificial Intelligence， 2023， 37（1）： 543-551. |
| [25] | LI J Y， CAO Z J， GAN Q， et al. Class-incremental SAR obeject detection via adaptive distributed response distillation［C］∥ IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium. Piscataway： IEEE Press， 2024： 9922-9925. |
| [26] | DONG N， ZHANG Y， DING M， et al. Bridging non-co-occurrence with unlabeled in-the-wild data for incremental object detection［C］∥ Proceedings of the 35th International Conference on Neural Information Processing Systems， New York：ACM， 2021：30492-30503. |
| [27] | LI D， TASCI S， GHOSH S， et al. RILOD： Near real-time incremental learning for object detection at the edge［C］∥ Proceedings of the 4th ACM/IEEE Symposium on Edge Computing. New York：ACM， 2019： 113-126. |
| [28] | CERMELLI F， GERACI A， FONTANEL D， et al. Modeling missing annotations for incremental learning in object detection［C］∥ 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW）. Piscataway： IEEE Press， 2022： 3699-3709. |
| [29] | WANG Y J， CHEN L Q， ZHAO T M， et al. High-dimension prototype is a better incremental object detection learner［C］∥ The Thirteenth International Conference on Learning Representations， 2025： 1-17. |
| [30] | WANG J B， CHEN Y M， ZHENG Z H， et al. CrossKD： Cross-head knowledge distillation for object detection［C］∥ 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2024： 16520-16530. |
| [31] | HAROON M， SHAHZAD M， FRAZ M M. Multisized object detection using spaceborne optical imagery［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2020， 13： 3032-3046. |
| [32] | 禹文奇，程塨，王美君，等.MAR20：遥感图像军用飞机目标识别数据集［J］.遥感学报，2023，27（12）：2688-2696. |
| YU W Q， CHENG G， WANG M J， et al. MAR20： A benchmark for military aircraft recognition in remote sensing images［J］. National Remote Sensing Bulletin， 2023， 27（12）： 2688-2696 （in Chinese）. | |
| [33] | LIU L Y， KUANG Z H， CHEN Y M， et al. IncDet： In defense of elastic weight consolidation for incremental object detection［J］. IEEE Transactions on Neural Networks and Learning Systems， 2021， 32（6）： 2306-2319. |
| [34] | MENEZES A G， DE MOURA G， ALVES C， et al. Continual object detection： A review of definitions， strategies， and challenges［J］. Neural Networks， 2023， 161： 476-493. |
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