融合岛式双向特征金字塔的遥感图像目标检测

doi:10.3778/j.issn.1002-8331.2406-0063

摘要/Abstract

摘要： 针对遥感图像目标检测存在复杂背景干扰、目标多尺度差异等问题，提出一种融合岛式双向特征金字塔网络的遥感图像目标检测算法（IFD-YOLOv8s）。设计岛式双向特征金字塔网络，增强模型对目标尺度变化的适应性，减少多层次特征融合过程中信息丢失，有助于深层语义和细粒度信息的高效传播；提出特征上下文增量模块，对地物目标特征进行更全面的捕获，提高模型检测能力；设计双线池化注意力模块，抑制非目标噪声干扰，增强遥感目标特征可辨别性。在公共数据集RSOD和NWPU VHR-10上进行消融和对比实验，平均准确率均值分别为98.2%和91.4%，相较于基线算法YOLOv8s分别提升1.8和2.1个百分点。与主流目标检测算法相比，IFD-YOLOv8s对复杂背景目标和多尺度目标的检测更有效。在公共数据集DOTA上进行泛化实验，平均准确率均值为78.7%，相比原模型提高1.8个百分点。

关键词: 遥感图像, 目标检测, 特征金字塔网络, 上下文信息, 注意力机制

Abstract: Aiming at the problems of object detection in remote sensing images, such as complex background interference and multi-scale differences of targets, a remote sensing image object detection that integrates island bi-directional feature pyramid network, referred to as IFD-YOLOv8s, is proposed. Firstly, an island bi-directional feature pyramid network is designed to enhance the adaptability of the model to target scale changes, reduce the information loss in the process of multilevel feature fusion, and contribute to the efficient propagation of deep semantic and fine-grained information; then a feature context incremental module is proposed to capture the feature of the feature target in a more comprehensive way, and to improve the model detection capability; and finally, a dual path pooling attention module is designed to inhibit the interference of non-target noise that enhances the remote sensing target feature discriminability. The ablation and comparison experiments are conducted on the public datasets RSOD and NWPU VHR-10, and the mean average precision are 98.2% and 91.4%, respectively, which are improved by 1.8 and 2.1 percentage points compared with the baseline algorithm YOLOv8s. Compared with mainstream object detection algorithms, IFD-YOLOv8s is more effective in detecting complex background targets and multi-scale targets. Generalization experiments on the public dataset DOTA show an mean average precision of 78.7%, which is a 1.8 percentage points improvement over the original model.

Key words: remote sensing image, object detection, feature pyramid network, contextual information, attention mechanism

梁礼明, 冯耀, 龙鹏威, 王泽欣. 融合岛式双向特征金字塔的遥感图像目标检测[J]. 计算机工程与应用, 2025, 61(19): 202-213.

LIANG Liming, FENG Yao, LONG Pengwei, WANG Zexin. Fusion of Island Bi-Directional Feature Pyramid for Remote Sensing Image Object Detection[J]. Computer Engineering and Applications, 2025, 61(19): 202-213.

参考文献

[1] 吴建成, 郭荣佐, 成嘉伟, 等. 注意力特征融合的快速遥感图像目标检测算法[J]. 计算机工程与应用, 2024, 60(1): 207-216.
WU J C, GUO R Z, CHENG J W, et al. Fast remote sensing image object detection algorithm based on attention feature fusion[J]. Computer Engineering and Applications, 2024, 60(1): 207-216.
[2] LIN J H, ZHAO Y, WANG S G, et al. YOLO-DA: an efficient YOLO-based detector for remote sensing object detection[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 6008705.
[3] SU Z H, YU J, TAN H T, et al. MSA-YOLO: a remote sensing object detection model based on multi-scale strip attention[J]. Sensors, 2023, 23(15): 6811.
[4] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2005: 886-893.
[5] SEDAGHAT A, EBADI H. Remote sensing image matching based on adaptive binning SIFT descriptor[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10): 5283-5293.
[6] HUI Y, YOU S J, HU X H, et al. SEB-YOLO: an improved YOLOv5 model for remote sensing small target detection[J]. Sensors, 2024, 24(7): 2193.
[7] 孟月波, 王菲, 刘光辉, 等. 多元特征提取与表征优化的遥感多尺度目标检测[J]. 光学精密工程, 2023, 31(16): 2465-2482.
MENG Y B, WANG F, LIU G H, et al. Remote sensing multi-scale object detection based on multivariate feature extraction and characterization optimization[J]. Optics and Precision Engineering, 2023, 31(16): 2465-2482.
[8] 梁礼明, 陈鑫, 余洁, 等. 多尺度注意力细化视网膜分割算法[J]. 计算机工程与应用, 2023, 59(6): 212-220.
LIANG L M, CHEN X, YU J, et al. Multi-scale attention refinement retinal segmentation algorithm[J]. Computer Engineering and Applications, 2023, 59(6): 212-220.
[9] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014: 580-587.
[10] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 1440-1448.
[11] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[12] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[13] FARHADI A, REDMON J. YOLOv3: an incremental improvement[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018.
[14] JOCHER G. YOLOv5 by Ultralytics (Version 7.0)[CP/OL]. (2020)[2024-06-03]. https://doi.org/10.5281/zenodo.3908559.
[15] LI C, LI L, JIANG H, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv:2209.
02976, 2022.
[16] WANG C Y, BOCHKOVSKIY A, LIAO H M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 7464-7475.
[17] WANG C Y, YEH I H, LIAO H Y M. YOLOv9: learning what you want to learn using programmable gradient information[J]. arXiv:2402.13616, 2024.
[18] BOCHKOVSKIY A, WANG C Y, LIAO H M, et al. YOLOv4: optimal speed and accuracy of object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 2-7.
[19] WANG K X, LIEW J H, ZOU Y T, et al. PANet: few-shot image semantic segmentation with prototype alignment[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 9196-9205.
[20] TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 10778-10787.
[21] JIANG Y, TAN Z, WANG J, et al. GiraffeDet: a heavy-neck paradigm for object detection[J]. arXiv:2202.04256, 2022.
[22] YANG G Y, LEI J, ZHU Z K, et al. AFPN: asymptotic feature pyramid network for object detection[C]//Proceedings of the 2023 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway: IEEE, 2023: 2184-2189.
[23] XU X, JIANG Y, CHEN W, et al. DAMO-YOLO: a report on real-time object detection design[J]. arXiv:2211.15444, 2022.
[24] SHANG J C, WANG J S, LIU S B, et al. Small target detection algorithm for UAV aerial photography based on improved YOLOv5s[J]. Electronics, 2023, 12(11): 2434.
[25] ZHANG J J, DING A Q, LI G Y, et al. A pyramid attention network with edge information injection for remote-sensing object detection[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 6007205.
[26] YI H, LIU B, ZHAO B, et al. Small object detection algorithm based on improved YOLOv8 for remote sensing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 17: 1734-1747.
[27] JOCHER G, CHAURASIA A, QIU J. Ultralytics YOLO (Version 8.0.0)[CP/OL]. (2023)[2024-06-03]. https://github.com/ultralytics/ultralytics.
[28] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 936-944.
[29] 梁礼明, 阳渊, 朱晨锟, 等. 融合Mobile Vit和倒置门控编解码的视网膜血管分割算法[J]. 北京航空航天大学学报, 2025, 51(3): 712-723.
LIANG L M, YANG Y, ZHU C K, et al. Fusion of Mobile Vit and inverted gated codec retinal vessel segmentation algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(3): 712-723.
[30] LAU K W, PO L M, REHMAN Y A U. Large separable kernel attention: rethinking the large kernel attention design in CNN[J]. Expert Systems with Applications, 2024, 236: 121352.
[31] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7132-7141.
[32] WANG Q L, WU B G, ZHU P F, et al. ECA-net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 11531-11539.
[33] HUANG H J, CHEN Z G, ZOU Y, et al. Channel prior convolutional attention for medical image segmentation[J]. Computers in Biology and Medicine, 2024, 178: 108784.