融合多尺度层级特征的航拍小目标检测

doi:10.3778/j.issn.1002-8331.2408-0105

摘要/Abstract

摘要： 针对航拍图像大视野、小尺寸、分布密集从而导致小目标检测精度低的问题，提出一种基于YOLOv8改进的融合多尺度特征的航拍检测算法，构建了轻量化的L-MobileViT模块捕获特征间的有效关系，减缓信息丢失，提高模型的检测性能。提出了多层级的多尺度融合模块HF（hierarchical fusion），融合深层级的语义信息与底层级空间纹理信息，提高密集场景下小目标的检测能力。在YOLOv8基础上增加小目标检测头删减大目标检测头，提升小目标检测能力，减少小目标的漏检。实验结果表明，改进后的模型在VisDrone2019与UAV航拍交通小目标数据集（UAV-TrafficTinyDataset）中取得了较优的效果，与基线模型相比，mAP@50分别提高17.6%、15.7%，对小目标的检测效果有明显的提升，综合性能优于主流的航拍检测算法，表明改进算法具有更优泛化性与鲁棒性，适用于航拍场景下的检测任务。

关键词: 航拍图像, 小目标检测, 多层级特征融合, L-MobileViT, YOLOv8

Abstract: Aiming at the problem of low accuracy in detecting small objects due to large field of view, small object size, and dense distribution in aerial images, a multi-scale feature fusion aerial detection algorithm based on improved YOLOv8 is proposed. Firstly, a lightweight L-MobileViT module is constructed to capture effective relationships between features, mitigate information loss, and improve the detection performance of the model. Secondly, a hierarchical multi-scale fusion module HF (hierarchical fusion) is proposed to integrate deep spatial information and shallow semantic information, enhancing the detection capability of small objects in dense scenes. Finally, a tiny detection head is added, and a large detection head is removed based on YOLOv8 to focus on the detection ability of small objects and reduce the missed detection rate of small objects. Experimental results show that the improved model achieves superior performance on the VisDrone2019 and UAV-TrafficTinyDataset datasets, with mAP@50 increased by 17.6% and 15.7%, respectively, compared to the baseline model. The detection effect of small objects is significantly improved, and the overall performance is better than mainstream aerial detection algorithms. This demonstrates that the improved algorithm has better generalization and robustness, making it suitable for detection tasks in aerial scenarios.

Key words: UAV images, small target detection, multi-level feature fusion, L-MobileViT, YOLOv8

杨鸿丹, 付贵, 邵慧超, 汪艺欣, 邵延华, 楚红雨, 邓琥. 融合多尺度层级特征的航拍小目标检测[J]. 计算机工程与应用, 2025, 61(9): 230-241.

YANG Hongdan, FU Gui, SHAO Huichao, WANG Yixin, SHAO Yanhua, CHU Hongyu, DENG Hu. Small Object Detection in Aerial Imagery Using Multi-Scale Hiearchical Feature Fusion Based Approach[J]. Computer Engineering and Applications, 2025, 61(9): 230-241.

参考文献

[1] 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.
[2] AMUDHAN A N, SUDHEER A P. Lightweight and computationally faster hypermetropic convolutional neural network for small size object detection[J]. Image and Vision Computing, 2022, 119: 104396.
[3] 邵延华, 张铎, 楚红雨, 等. 基于深度学习的YOLO目标检测综述[J]. 电子与信息学报, 2022, 44(10): 3697-3708.
SHAO Y H, ZHANG D, CHU H Y, et al. A review of YOLO object detection based on deep learning[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3697-3708.
[4] 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, 2017, 39(6): 1137-1149.
[5] 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.
[6] 张智, 易华挥, 郑锦. 聚焦小目标的航拍图像目标检测算法[J]. 电子学报, 2023, 51(4): 944-955.
ZHANG Z, YI H H, ZHENG J. Focusing on small objects detector in aerial images[J]. Acta Electronica Sinica, 2023, 51(4): 944-955.
[7] LIN T, 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 (CVPR), 2017: 936-944.
[8] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768.
[9] SANDLER M, HOWARD A, ZHU M, et al. MobileNetv2: inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 4510-4520.
[10] MA N, ZHANG X, ZHENG H T, et al. ShuffleNet v2: practical guidelines for efficient CNN architecture design[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 116-131.
[11] DENG S, LI S, XIE K, et al. A global-local self-adaptive network for drone-view object detection[J]. IEEE Transactions on Image Processing, 2020, 30: 1556-1569.
[12] WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 390-391.
[13] YE T, QIN W, ZHAO Z, et al. Real-time object detection network in UAV-vision based on CNN and transformer[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-13.
[14] TAN M, PANG R, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020: 10781-10790.
[15] YANG G, LEI J, ZHU Z, et al. AFPN: asymptotic feature pyramid network for object detection[C]//Proceedings of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023: 2184-2189.
[16] 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.
[17] ZHU X, LYU S, 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 (ICCVW), 2021: 2778-2788.
[18] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[19] YANG Y, GAO X, WANG Y, et al. VAMYOLOX: an accurate and efficient object detection algorithm based on visual attention mechanism for UAV optical sensors[J]. IEEE Sensors Journal, 2022, 23(11): 11139-11155.
[20] MISRA D, NALAMADA T, ARASANIPALAI A U, et al. Rotate to attend: convolutional triplet attention module[C]// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021: 3139-3148.
[21] LIU W, QUIJANO K, CRAWFORD M M. YOLOv5-tassel: detecting tassels in RGB UAV imagery with improved YOLOv5 based on transfer learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 8085-8094.
[22] YANG L, ZHANG R Y, LI L, et al. SimAM: a simple, parameter-free attention module for convolutional neural networks[C]//Proceedings of the International Conference on Machine Learning, 2021: 11863-11874.
[23] 李翔, 张涛, 张哲, 等. Transformer在计算机视觉领域的研究综述[J]. 计算机工程与应用, 2023, 59(1): 1-14.
LI X, ZHANG T, ZHANG Z, et al. Survey of Transformer research in computer vision[J]. Computer Engineering and Applications, 2023, 59(1): 1-14.
[24] MEHTA S, RASTEGARI M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer[J]. arXiv:2110.02178, 2021.
[25] 付锦燚, 张自嘉, 孙伟, 等. 改进YOLOv8的航拍图像小目标检测算法[J]. 计算机工程与应用, 2024, 60(6): 100-109.
FU J Y, ZHANG Z J, SUN W, et al. Improved YOLOv8 small target detection algorithm in aerial images[J]. Computer Engineering and Applications, 2024, 60(6): 100-109.
[26] LIU W, LU H, FU H, et al. Learning to upsample by learning to sample[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023: 6027-6037.
[27] HOWARD A G. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017.
[28] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]//Proceedings of the 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014: 740-755.
[29] DU D, ZHU P, WEN L, 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 (ICCVW), 2019: 213-226.
[30] 徐慧智, 古旭楠. 无人机视角下交通小目标图像检测算法优化研究[J]. 计算机工程与应用, 2024, 60(21): 194-204.
XU H Z, GU X N. Research on optimization of UAV traffic small target image detection algorithm[J]. Computer Engineering and Applications, 2024, 60(21): 194-204.
[31] ZHANG P, ZHONG Y, LI X. SlimYOLOv3: narrower, faster and better for real-time UAV applications[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW2019), 2019: 1-9.
[32] JOCHER G, STOKEN A, BOROVEC J, et al. ultralytics/yolov5: v5. 0-YOLOv5-P6 1280 models, AWS, Supervise. ly and YouTube integrations[J]. Zenodo, 2021.
[33] XU S, WANG X, LV W, et al. PP-YOLOE: an evolved version of YOLO[J]. arXiv:2203.16250, 2022.
[34] LIU S, ZHA J, SUN J, et al. EdgeYOLO: an edge-real-time object detector[C]//Proceedings of the 2023 42nd Chinese Control Conference (CCC), 2023: 7507-7512.
[35] LIANG S, WU H, ZHEN L, et al. Edge YOLO: real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 25345-25360.
[36] WU Q, LI Y, HUANG W, et al. C3TB-YOLOv5: integrated YOLOv5 with transformer for object detection in high-resolution remote sensing images[J]. International Journal of Remote Sensing, 2024, 45(8): 2622-2650.
[37] TANG W, SUN J, WANG G. Horizontal feature pyramid network for object detection in UAV images[C]//Proceedings of the 2021 China Automation Congress (CAC), 2021: 7746-7750.
[38] LIU Z, LIN Y, CAO Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 10012-10022.
[39] ZHU X, SU W, LU L, et al. Deformable DETR: deformable Transformers for end-to-end object detection[C]//Proceedings of the 37th International Conference on Logic Programming(ICLP2021), 2021: 1-16.
[40] 于傲泽, 魏维伟, 王平, 等. 基于分块复合注意力的无人机小目标检测算法[J]. 航空学报, 2024, 45(14): 629148.
YU A Z, WEI W W, WANG P, et al. Small target detection algorithm for UAV based on patch-wise co-attention[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(14): 629148.
[41] ZHANG Z. Drone-YOLO: an efficient neural network method for target detection in drone images[J]. Drones, 2023, 7(8): 526.
[42] LOU H, DUAN X, GUO J, et al. DC-YOLOv8: small-size object detection algorithm based on camera sensor[J]. Electronics, 2023, 12(10): 2323.