改进YOLOv8的无人机航拍图像目标检测算法

doi:10.3778/j.issn.1002-8331.2405-0459

摘要/Abstract

摘要： 针对无人机航拍图像存在多个小目标聚集、目标尺度变化大的问题，提出一种改进YOLOv8的目标检测算法TS-YOLO（tiny and scale-YOLO）。在主干部分去除冗余的特征提取层，设计了一种高效特征提取模块（efficient feature extraction module，EFEM），避免小目标特征消失在冗余信息中。在颈部设计了一种双重跨尺度加权特征融合方法（dual cross-scale weighted feature-fusion，DCWF），融合多尺度信息的同时抑制噪声干扰，提升特征表达能力。通过构建一种参数共享检测头（parameter-shared detection header，PSDH），使回归和分类任务实现参数共享，保证检测精度的同时有效降低了模型的参数量。所提模型在VisDrone-2019数据集上的精度（P）和召回率（R）分别达到54.0%、42.5%；相比于原始YOLOv8s模型，mAP50提高了5.0个百分点，达到44.5%，且参数量减少了55.8%，仅有4.94×106；在DOTAv1.0遥感数据集上，mAP50达到71.9%，仍具有较好的泛化能力。

关键词: 目标检测, 无人机航拍图像, YOLOv8, 小目标, 特征融合

Abstract: Aiming at the problems of multiple small targets aggregation and large target scale variation in UAV aerial images, an improved YOLOv8 target detection algorithm named TS-YOLO (tiny and scale-YOLO) is proposed. Firstly, the redundant feature extraction layer is removed in the backbone part, and an efficient feature extraction module (EFEM) is designed to avoid small target features disappearing in redundant information. Secondly, a dual cross-scale weighted feature-fusion (DCWF) method is adopted in the neck, which fuses the multi-scale information and suppresses the noise interference for improving the feature expression ability. Finally, by constructing a parameter-shared detection header (PSDH), the regression and classification is took to realize parameter sharing, which ensures the detection accuracy and effectively reduces the number of parameters in the model. The precision (P) and recall (R) of the proposed model on the VisDrone-2019 dataset reach to 54.0% and 42.5%, respectively; compared with the original YOLOv8s model, the mAP50 is improved by 5.0 percentage points to 44.5% and the parameter quantity is reduced by 55.8% to only 4.94×106; on the DOTAv1.0 remote sensing dataset, the mAP50 reaches 71.9%, which still has good generalization ability.

Key words: target detection, unmanned aerial vehicles images, YOLOv8, small target, feature fusion

梁燕, 何孝武, 邵凯, 陈俊宏. 改进YOLOv8的无人机航拍图像目标检测算法[J]. 计算机工程与应用, 2025, 61(1): 121-130.

LIANG Yan, HE Xiaowu, SHAO Kai, CHEN Junhong. Target Detection Algorithm for UAV Images Based on Improved YOLOv8[J]. Computer Engineering and Applications, 2025, 61(1): 121-130.

参考文献

[1] KISANTAL M, WOJNNA Z, MURAWSKI J, et al. Augmentation for small object detection[EB/OL]. (2019-02-19)[2024-03-11]. https://doi.org/10.48550/arXiv.1902.07296.
[2] 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, 2014: 580-587.
[3] 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 & Machine Intelligence, 2017, 39(6): 1137-1149.
[4] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL].(2018-04-08)[2024-03-11]. https://doi.org/10. 48550/arXiv.1804.02767.
[5] GE Z, LIU S T, WANG F, et al. YOLOX: exceeding YOLO series in 2021[EB/OL].(2021-07-18) [2024-03-11]. https://doi.org/10.48550/arXiv.2107.08430.
[6] 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 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.
[7] 冷佳旭, 莫梦竟成, 周应华, 等. 无人机视角下的目标检测研究进展[J]. 中国图象图形学报, 2023, 28(9): 2563-2586.
LENG J X, MO M J C, ZHOU Y H, et al. Recent advances in drone-view object detection[J]. Journal of Image and Graphics, 2023, 28(9): 2563-2586.
[8] 蒋伟, 王万虎, 杨俊杰. AEM-YOLOv8s: 无人机航拍图像的小目标检测[J].计算机工程与应用, 2024, 60(17): 191-202.
JIANG W, WANG W H, YANG J J. AEM-YOLOv8s: small target detection algorithm for UAV aerial images[J]. Computer Engineering and Applications, 2024, 60(17): 191-202.
[9] PENG G L, YANG Z J, WANG S B, et al. AMFLW-YOLO: a lightweight network for remote sensing image detection based on attention mechanism and multiscale feature fusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4600916.
[10] 梁燕, 易春霞, 王光宇, 等. 基于多尺度语义编解码网络的遥感图像语义分割[J]. 电子学报, 2023, 51(11): 3199-3214.
LIANG Y, YI C X, WANG G Y, et al. Semantic segmentation of remote sensing image based on multi-scale semantic encoder-decoder network[J]. Acta Electronica Sinica, 2023, 51(11): 3199-3214.
[11] 彭晏飞, 赵涛, 陈炎康, 等. 基于上下文信息与特征细化的无人机小目标检测算法[J]. 计算机工程与应用, 2024, 60(5): 183-190.
PENG Y F, ZHAO T, CHEN Y K, et al. UAV small object detection algorithm based on context information and feature refinement[J]. Computer Engineering and Applications, 2024, 60(5): 183-190.
[12] QIN Z Y, CHEN D K, WANG H Y. MCA-YOLOv7: an improved UAV target detection algorithm based on YOLOv7[J]. IEEE Access, 2024, 12: 42642-42650.
[13] 梁燕, 易春霞, 王光宇. 基于编解码网络UNet3+的遥感影像建筑变化检测[J]. 计算机学报, 2023, 46(8): 1720-1733.
LIANG Y, YI C X, WANG G Y. Detection of building change in remote sensing image based on encoder-decoder network UNet3+[J]. Chinese Journal of Computers, 2023, 46(8): 1720-1733.
[14] GUO Z H, GUO L J. Small target detection in aerial photography based on improved YOLOv5[C]//Proceedings of the 2023 3rd International Conference on Robotics, Automation and Intelligent Control, 2024: 409-413.
[15] LAN Z Y, ZHUANG F Y, LIN Z J, et al. MFO-Net: a multiscale feature optimization network for UAV image object detection[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1-5.
[16] 李岩超, 史卫亚, 冯灿.面向无人机航拍小目标检测的轻量级YOLOv8检测算法[J]. 计算机工程与应用, 2024, 60(17): 167-178.
LI Y C, SHI W Y, FENG C. Lightweight YOLOv8 detection algorithm for small object detection in UAV aerial photography[J]. Computer Engineering and Applications, 2024, 60(17): 167-178.
[17] 单慧琳, 王硕洋, 童俊毅, 等. 增强小目标特征的多尺度光学遥感图像目标检测[J]. 光学学报, 2024, 44(6): 374-386.
SHAN H L, WANG S Y, TONG J Y, et al. Multi-scale optical remote sensing image target detection based on enhanced small target features[J]. Chinese Journal of Lasers, 2024, 44(6): 374-386.
[18] DAI X Y, CHEN Y P, XIAO B, et al. Dynamic head: unifying object detection heads with attentions[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 7369-7378.
[19] ZHENG Q, TIAN X, YU Z, et al. MobileRaT: a lightweight radio transformer method for automatic modulation classification in drone communication systems[J]. Drones, 2023, 7(10): 596.
[20] ZHENG Q, SAPONARA S, TIAN X, et al. A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT[J]. Cogn Neurodyn, 2024(18): 659-671.
[21] 徐彦威, 李军, 董元方, 等.YOLO系列目标检测算法综述[J].计算机科学与探索, 2024, 18(9): 2221-2238.
XU Y W, LI J, DONG Y F, et al. Survey of development of YOLO object detection algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(9): 2221-2238.
[22] CHEN J R, KAO S H, HE H, et al. Run, don’t walk: chasing higher FLOPS for faster neural networks[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 12021-12031.
[23] CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1800-1807.
[24] TAN M X, PANG R M, LE Q. EfficientDet: scalable and efficient object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 10778-10787.
[25] DU D W, ZHU P F, WEN L Y, et al. VisDrone-DET2019: the vision meets drone object detection in image challenge results[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshop, 2019: 213-226.
[26] XIA G S, BAI X, DING J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3974-3983.
[27] 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.
[28] XU X, JIANG Y, CHEN W, et al. DAMO-YOLO: a report on real-time object detection design[J]. arXiv:2211.15444,2022.
[29] ZHAO Y A, LV W Y, XU S L, et al. DETRs beat YOLOs on real-time object detection[J]. arXiv:2304.08069, 2023.