多尺度特征聚合扩散和边缘信息增强的小目标检测算法

doi:10.3778/j.issn.1002-8331.2410-0084

摘要/Abstract

摘要： 无人机航拍图像中，目标尺寸变化剧烈、背景复杂且小目标比例较高等特点为目标检测任务带来巨大挑战。尽管现有的基于卷积的目标检测算法能有效获取空间信息，但在实现不同尺度特征的全局交互及边缘细节信息的有效利用上仍存在不足。因此，提出了一种结合多尺度特征聚合扩散和边缘信息增强的小目标检测算法ADE-YOLO。构建了多尺度特征聚合扩散金字塔网络（MFADPN），通过在中间层聚合不同层级特征，并将其直接扩散至相邻层以缩短传播路径，有效减少了信息在传递过程中的损失，增强了模型的多尺度表达能力，显著提升了对不同尺度目标的检测能力。设计了自适应上下文融合模块（ACFM），利用通道注意力机制自适应地调整不同特征图的贡献，进一步强化多尺度特征的融合效果，使得重要特征在信息融合过程中更加突出。提出的C2f-Sobel模块通过额外分支结合Sobel算子来提取图像的边缘信息，从而为模型提供了更丰富的细节信息，提升了其在复杂场景下目标定位能力。实验结果表明，ADE-YOLO相较于基线YOLOv10s，在VisDrone2019和TinyPerson数据集上分别提高了8.6个百分点和4.0个百分点（mAP0.5），并且在与其他先进模型的对比中也展示了显著的优势。

关键词: 小目标检测, 航拍图像, 特征金字塔, 自适应特征融合, 边缘信息

Abstract: 1.College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
2.Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China
3.Technology Innovation Center of Factored Transaction Data in Tourist Attractions, Ministry of Culture and Tourism, Fuzhou 350000, China
4.Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100084, China

Key words: small object detection, aerial images, feature pyramid, adaptive feature fusion, edge information

江旺玉, 王乐, 姚叶鹏, 毛国君. 多尺度特征聚合扩散和边缘信息增强的小目标检测算法[J]. 计算机工程与应用, 2025, 61(7): 105-116.

JIANG Wangyu, WANG Le, YAO Yepeng, MAO Guojun. Multi-Scale Feature Aggregation Diffusion and Edge Information Enhancement Small Object Detection Algorithm[J]. Computer Engineering and Applications, 2025, 61(7): 105-116.

参考文献

[1] SUN W, DAI L, ZHANG X, et al. RSOD: real-time small object detection algorithm in UAV-based traffic monitoring[J]. Applied Intelligence, 2022: 1-16.
[2] BYUN S, SHIN I K, MOON J, et al. Road traffic monitoring from UAV images using deep learning networks[J]. Remote Sensing, 2021, 13(20): 4027.
[3] PRATAMA M T, KIM S, OZAWA S, et al. Deep learning-based object detection for crop monitoring in soybean fields[C]//Proceedings of the 2020 International Joint Conference on Neural Networks, 2020: 1-7.
[4] AMIT S N K B, AOKI Y. Disaster detection from aerial imagery with convolutional neural network[C]//Proceedings of the 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing, 2017: 239-245.
[5] PI Y, NATH N D, BEHZADAN A H. Convolutional neural networks for object detection in aerial imagery for disaster response and recovery[J]. Advanced Engineering Informatics, 2020, 43: 101009.
[6] REDMON J. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
[7] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real -time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.
[8] WANG A, CHEN H, LIU L, et al. YOLOv10: real-time end-to-end object detection[J]. arXiv:2405.14458, 2024.
[9] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21-37.
[10] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
[11] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
[12] REN S, HE K, 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, 2016, 39(6): 1137-1149.
[13] TANG G, NI J, ZHAO Y, et al. A survey of object detection for UAVs based on deep learning[J]. Remote Sensing, 2023, 16(1): 149.
[14] GAO T, NIU Q Q, ZHANG J, et al. Global to local: a scale-aware network for remote sensing object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5615614.
[15] ZENG S, YANG W Z, JIAO Y Y, et al. SCA-YOLO: a new small object detection model for UAV images[J]. The Visual Computer, 2024, 40(3): 1787-1803.
[16] XI Y, JIA W J, MIAO Q G, et al. FiFoNet: fine-grained target focusing network for object detection in UAV images[J]. Remote Sensing, 2022, 14(16): 3919.
[17] LI J, XIE C, WU S Z, et al. UAV-YOLOv5: a swin-transformer-enabled small object detection model for long-range UAV images[J]. Annals of Data Science, 2024, 11(4): 1109-1138.
[18] ZHU X K, LYU S C, WANG X, et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops, 2021: 2778-2788.
[19] JIANG L J, YUAN B X, DU J W, et al. MFFSODNet: multiscale feature fusion small object detection network for UAV aerial images[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 5015214.
[20] WANG Y M, ZOU H, YIN M, et al. SMFF-YOLO: a scale-adaptive YOLO algorithm with multi-level feature fusion for object detection in UAV scenes[J]. Remote Sensing, 2023, 15(18): 4580.
[21] 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.
[22] LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 8759-8768.
[23] DONG D H, LI J Z, LIU H Y, et al. EA-YOLO: an efficient and accurate UAV image object detection algorithm[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2025, 20(1): 61-68.
[24] CAO Y R, HE Z J, WANG L J, et al. VisDrone-DET2021: the vision meets drone object detection challenge results[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops, 2021: 213-226.
[25] WALAMBE R, MARATHE A, KOTECHA K. Multiscale object detection from drone imagery using ensemble transfer learning[J]. Drones, 2021, 5(3): 66.
[26] YU X H, GONG Y Q, JIANG N, et al. Scale match for tiny person detection[C]//Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2020: 1246-1254.
[27] ZHANG L H, XIONG N, PAN X H, et al. Improved object detection method utilizing YOLOv7-tiny for unmanned aerial vehicle photographic imagery[J]. Algorithms, 2023, 16(11): 520.
[28] ZHANG Z X. Drone-YOLO: an efficient neural network method for target detection in drone images[J]. Drones, 2023, 7(8): 526.
[29] MA C J, FU Y Y, WANG D Y, et al. YOLO-UAV: object detection method of unmanned aerial vehicle imagery based on efficient multi-scale feature fusion[J]. IEEE Access, 2023, 11: 126857-126878.
[30] LIU H Y, DUAN X H, CHEN H N, et al. DBF-YOLO: UAV small targets detection based on shallow feature fusion[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2023, 18(4): 605-612.
[31] TANG S Y, ZHANG S, FANG Y N. HIC-YOLOv5: improved YOLOv5 for small object detection[C]//Proceedings of the 2024 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2024: 6614-6619.
[32] 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, 2020.
[33] WANG C C, HE W, NIE Y, et al. Gold-YOLO: efficient object detector via gather-and-distribute mechanism[C]//Advances in Neural Information Processing Systems 36 , 2024.