Super-Resolution Enhancement and Scene Context Collaborative Dense Tiny Object Detection in Remote Sensing: Research Progress and Integrated Optimization Paradigm
Keywords:
High-resolution Remote Sensing Imagery; Dense Tiny Objects; Super-resolution Reconstruction; Scene Context; Cascaded Detection; Multi-scale Feature Fusion.Abstract
Dense tiny object detection in high-resolution remote sensing images serves as a core technical bottleneck in the intelligent interpretation of remote sensing data, with critical application value across maritime supervision, urban traffic management, emergency rescue, national land security and other scenarios. Aiming at the limitations of existing research that mostly conducts optimization from a single technical dimension, suffers insufficient coordination among multi-path approaches, and exhibits severe performance degradation in dense scenes, this paper systematically reviews the technical evolution of remote sensing tiny object detection in the deep learning era. In-depth analysis is conducted along five major technical avenues: pre-processing super-resolution enhancement, multi-scale feature fusion, contextual awareness modeling, cascaded position refinement, and Transformer global modeling. The technical principles, implementation schemes, applicable scenarios and performance boundaries of various methods are compared. On this basis, an integrated optimization framework combining lightweight pre-processing super-resolution enhancement, scene context-guided feature pyramid, and cascaded object refinement head is proposed. Deep collaboration across multiple technical pathways compensates for the inherent defects of individual approaches. Experiments demonstrate that the proposed integrated framework can significantly boost detection accuracy in typical dense tiny object scenarios such as port ships and urban vehicles, while only introducing marginal computational overhead. This work provides novel optimization ideas and practical references for the engineering deployment of remote sensing tiny object detection technology.
References
Bashir, S. M. A., & Wang, Y. (2021). Small object detection in remote sensing images with residual feature aggregation-based super-resolution and object detector network. Remote Sensing, 13(9), 1854.https://doi.org/10.3390/rs13091854
Chen, G., Wang, H., Chen, K., Li, Z., Song, Z., Liu, Y., & Knoll, A. (2020). A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(2), 936–953. https://doi.org/10.1109/TSMC.2020.3005231
Chen, Z., Ji, H., Zhang, Y., Zhu, Z., & Li, Y. (2023). High-resolution feature pyramid network for small object detection on drone view. IEEE Transactions on Circuits and Systems for Video Technology, 34(1), 475–489. https://doi.org/10.1109/TCSVT.2023.3286896
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, 45(11), 13467–13488. https://doi.org/10.1109/TPAMI.2023.3290594
Courtrai, L., Pham, M. T., & Lefèvre, S. (2020). Small object detection in remote sensing images based on super-resolution with auxiliary generative adversarial networks. Remote Sensing, 12(19), 3152. https://doi.org/10.3390/rs12193152
Dai, Y., Li, X., Zhou, F., Qian, Y., Chen, Y., & Yang, J. (2023). One-stage cascade refinement networks for infrared small target detection. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–17. https://doi.org/10.1109/TGRS.2023.3243062
Fan, X., Hu, Z., Zhao, Y., Chen, J., Wei, T., & Huang, Z. (2024). A small-ship object detection method for satellite remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 11886–11898. https://doi.org/10.1109/JSTARS.2024.3419786
Gong, H., Mu, T., Li, Q., Dai, H., Li, C., He, Z., & Wang, B. (2022). Swin-transformer-enabled YOLOv5 with attention mechanism for small object detection on satellite images. Remote Sensing, 14(12), 2861. https://doi.org/10.3390/rs14122861
Gui, S., Song, S., Qin, R., & Tang, Y. (2024). Remote sensing object detection in the deep learning era—a review. Remote Sensing, 16(2), 327. https://doi.org/10.3390/rs16020327
Han, W., Chen, J., Wang, L., Feng, R., Li, F., Wu, L., & Yan, J. (2021). Methods for small, weak object detection in optical high-resolution remote sensing images: A survey of advances and challenges. IEEE Geoscience and Remote Sensing Magazine, 9(4), 8–34. https://doi.org/10.1109/MGRS.2020.3041450
Han, W., Fan, R., Wang, L., Feng, R., Li, F., Deng, Z., & Chen, X. (2020). Improving training instance quality in aerial image object detection with a sampling-balance-based multistage network. IEEE Transactions on Geoscience and Remote Sensing, 59(12), 10575–10589. https://doi.org/10.1109/TGRS.2020.3038803
Hou, L., Lu, K., & Xue, J. (2022). Refined one-stage oriented object detection method for remote sensing images. IEEE Transactions on Image Processing, 31, 1545–1558. https://doi.org/10.1109/TIP.2022.3143690
Hua, W., & Chen, Q. (2025). A survey of small object detection based on deep learning in aerial images. Artificial Intelligence Review, 58(6), 162. https://doi.org/10.1007/s10462-025-11150-9
Jin, X., Tong, A., Ge, X., Ma, H., Li, J., Fu, H., & Gao, L. (2024). YOLOv7-bw: A dense small object efficient detector based on remote sensing image. ICCK Transactions on Intelligent Systematics, 1(1), 30–39. https://doi.org/10.62762/TIS.2024.137321
Kong, Y., Shang, X., & Jia, S. (2024). Drone-DETR: Efficient small object detection for remote sensing image using enhanced RT-DETR model. Sensors, 24(17), 5496. https://doi.org/10.3390/s24175496
Koyun, O. C., Keser, R. K., Akkaya, I. B., & Töreyin, B. U. (2022). Focus-and-Detect: A small object detection framework for aerial images. Signal Processing: Image Communication, 104, 116675. https://doi.org/10.1016/j.image.2022.116675
Li, Y., Huang, Q., Pei, X., Chen, Y., Jiao, L., & Shang, R. (2020). Cross-layer attention network for small object detection in remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2148–2161. https://doi.org/10.1109/JSTARS.2020.3046482
Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., & Gao, Y. (2022). Deep learning-based object detection techniques for remote sensing images: A survey. Remote Sensing, 14(10), 2385. https://doi.org/10.3390/rs14102385
Liu, C., Zhang, S., Hu, M., & Song, Q. (2024). Object detection in remote sensing images based on adaptive multi-scale feature fusion method. Remote Sensing, 16(5), 907. https://doi.org/10.3390/rs16050907
Liu, D., Zhang, J., Qi, Y., Wu, Y., & Zhang, Y. (2024). Tiny object detection in remote sensing images based on object reconstruction and multiple receptive field adaptive feature enhancement. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–13. https://doi.org/10.1109/TGRS.2024.3381774
Liu, J., Zheng, K., Liu, X., Xu, P., & Zhou, Y. (2024). SDSDet: A real-time object detector for small, dense, multi-scale remote sensing objects. Image and Vision Computing, 142, 104898. https://doi.org/10.1016/j.imavis.2024.104898
Liu, Y., Xiong, Z., Yuan, Y., & Wang, Q. (2023). Distilling knowledge from super-resolution for efficient remote sensing salient object detection. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–16. https://doi.org/10.1109/TGRS.2023.3267271
Liu, Y., Yang, D., Song, T., Ye, Y., & Zhang, X. (2025). YOLO-SSP: an object detection model based on pyramid spatial attention and improved downsampling strategy for remote sensing images. The Visual Computer, 41(3), 1467–1482. https://doi.org/10.1007/s00371-024-03672-0
Lu, X., Ji, J., Xing, Z., & Miao, Q. (2021). Attention and feature fusion SSD for remote sensing object detection. IEEE Transactions on Instrumentation and Measurement, 70, 1–9. https://doi.org/10.1109/TIM.2021.3052575
Ma, M., & Pang, H. (2023). SP-YOLOv8s: An improved YOLOv8s model for remote sensing image tiny object detection. Applied Sciences, 13(14), 8161. https://doi.org/10.3390/app13148161
Ma, S., Zhang, Y., Peng, L., Sun, C., Ding, B., & Zhu, Y. (2025). OWRT-DETR: A novel real-time transformer network for small-object detection in open-water search and rescue from UAV aerial imagery. IEEE Transactions on Geoscience and Remote Sensing, 63, 1–13. https://doi.org/10.1109/TGRS.2025.3560928
Ning, X., Zhang, H., Zhang, R., & Huang, X. (2024). Multi-stage progressive change detection on high resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 207, 231–244. https://doi.org/10.1016/j.isprsjprs.2024.01.016
Peng, D., Bruzzone, L., Zhang, Y., Guan, H., & He, P. (2021). SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 103, 102465. https://doi.org/10.1016/j.jag.2021.102465
Pham, M. T., Courtrai, L., Friguet, C., Lefèvre, S., & Baussard, A. (2020). YOLO-Fine: One-stage detector of small objects under various backgrounds in remote sensing images. Remote Sensing, 12(15), 2501. https://doi.org/10.3390/rs12152501
Qi, G., Zhang, Y., Wang, K., Mazur, N., Liu, Y., & Malaviya, D. (2022). Small object detection method based on adaptive spatial parallel convolution and fast multi-scale fusion. Remote Sensing, 14(2), 420. https://doi.org/10.3390/rs14020420
Qu, J., Tang, Z., Zhang, L., Zhang, Y., & Zhang, Z. (2023). Remote sensing small object detection network based on attention mechanism and multi-scale feature fusion. Remote Sensing, 15(11), 2728. https://doi.org/10.3390/rs15112728
Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., & Chao, D. (2020). Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network. Remote Sensing, 12(9), 1432. https://doi.org/10.3390/rs12091432
Wan, D., Lu, R., Wang, S., Shen, S., Xu, T., & Lang, X. (2023). Yolo-hr: Improved yolov5 for object detection in high-resolution optical remote sensing images. Remote Sensing, 15(3), 614. https://doi.org/10.3390/rs15030614
Wang, G., Zhuang, Y., Chen, H., Liu, X., Zhang, T., Li, L., & Sang, Q. (2021). FSoD-Net: Full-scale object detection from optical remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–18. https://doi.org/10.1109/TGRS.2021.3064599
Wang, X., Wang, A., Yi, J., Song, Y., & Chehri, A. (2023). Small object detection based on deep learning for remote sensing: A comprehensive review. Remote Sensing, 15(13), 3265. https://doi.org/10.3390/rs15133265
Wang, Y., Cui, L., Zhang, C., Chen, W., Xu, Y., & Zhang, Q. (2022). A two-stage seismic damage assessment method for small, dense, and imbalanced buildings in remote sensing images. Remote Sensing, 14(4), 1012. https://doi.org/10.3390/rs14041012
Wang, Z. Z., Xie, K., Zhang, X. Y., Chen, H. Q., Wen, C., & He, J. B. (2021). Small-object detection based on yolo and dense block via image super-resolution. IEEE Access, 9, 56416–56429. https://doi.org/10.1109/ACCESS.2021.3072211
Wei, W., Cheng, Y., He, J., & Zhu, X. (2024). A review of small object detection based on deep learning. Neural Computing and Applications, 36(12), 6283–6303. https://doi.org/10.1007/s00521-024-09422-6
Wu, B., Shen, Y., Guo, S., Chen, J., Sun, L., Li, H., & Ao, Y. (2022). High quality object detection for multiresolution remote sensing imagery using cascaded multi-stage detectors. Remote Sensing, 14(9), 2091. https://doi.org/10.3390/rs14092091
Wu, J., & Xu, S. (2021). From point to region: Accurate and efficient hierarchical small object detection in low-resolution remote sensing images. Remote Sensing, 13(13), 2620. https://doi.org/10.3390/rs13132620
Wu, J., Pan, Z., Lei, B., & Hu, Y. (2022). FSANet: Feature-and-spatial-aligned network for tiny object detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–17. https://doi.org/10.1109/TGRS.2022.3205052
Xiaolin, F., Fan, H., Ming, Y., Tongxin, Z., Ran, B., Zenghui, Z., & Zhiyuan, G. (2022). Small object detection in remote sensing images based on super-resolution. Pattern Recognition Letters, 153, 107–112. https://doi.org/10.1016/j.patrec.2021.12.011
Xie, X., Cheng, G., Li, Q., Miao, S., Li, K., & Han, J. (2024). Fewer is more: Efficient object detection in large aerial images. Science China Information Sciences, 67(1), 112106. https://doi.org/10.1007/s11432-022-3718-5
Xu, D., & Wu, Y. (2020). Improved YOLO-V3 with DenseNet for multi-scale remote sensing target detection. Sensors, 20(15), 4276. https://doi.org/10.3390/s20154276
Yang, X., Zhang, X., Wang, N., & Gao, X. (2021). A robust one-stage detector for multiscale ship detection with complex background in massive SAR images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–12. https://doi.org/10.1109/TGRS.2021.3128060
Ye, T., Qin, W., Li, Y., Wang, S., Zhang, J., & Zhao, Z. (2022). Dense and small object detection in UAV-vision based on a global-local feature enhanced network. IEEE Transactions on Instrumentation and Measurement, 71, 1–13. https://doi.org/10.1109/TIM.2022.3196319
Yu, D., & Ji, S. (2021). A new spatial-oriented object detection framework for remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–16. https://doi.org/10.1109/TGRS.2021.3127232
Yuan, X., Cheng, G., Li, G., Dai, W., Yin, W., Feng, Y., & Han, J. (2023). Progress in small object detection for remote sensing images. Journal of Image and Graphics, 28(6), 1662–1684. https://doi.org/10.11834/jig.221202
Zhang, C., Lam, K. M., & Wang, Q. (2023). CoF-Net: A progressive coarse-to-fine framework for object detection in remote-sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–17. https://doi.org/10.1109/TGRS.2022.3233881
Zhang, C., Lam, K. M., Liu, T., Chan, Y. L., & Wang, Q. (2024). Structured adversarial self-supervised learning for robust object detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–20. https://doi.org/10.1109/TGRS.2024.3375398
Zhang, J., Lei, J., Xie, W., Fang, Z., Li, Y., & Du, Q. (2023). SuperYOLO: Super resolution assisted object detection in multimodal remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–15. https://doi.org/10.1109/TGRS.2023.3258666
Zhang, T., Zhang, X., Zhu, X., Wang, G., Han, X., Tang, X., & Jiao, L. (2024). Multistage enhancement network for tiny object detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–12. https://doi.org/10.1109/TGRS.2024.3363614
Zhang, X., Han, L., Han, L., & Zhu, L. (2020). How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery?. Remote Sensing, 12(3), 417. https://doi.org/10.3390/rs12030417
Zhang, X., Zhang, T., Wang, G., Zhu, P., Tang, X., Jia, X., & Jiao, L. (2023). Remote sensing object detection meets deep learning: A metareview of challenges and advances. IEEE Geoscience and Remote Sensing Magazine, 11(4), 8–44. https://doi.org/10.1109/MGRS.2023.3312347
Zhao, Z., Du, J., Li, C., Fang, X., Xiao, Y., & Tang, J. (2024). Dense tiny object detection: A scene context guided approach and a unified benchmark. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–13. https://doi.org/10.1109/TGRS.2024.3357706
