改进YOLOv8的航拍小目标检测方法：CRP-YOLO

doi:10.3778/j.issn.1002-8331.2401-0474

摘要/Abstract

摘要： 无人机航拍目标检测是近些年研究的热点，由于无人机航拍视角下的小目标图像及被遮挡情况严重，导致出现漏检、误检等问题。针对以上问题，提出了一种改进YOLOv8的航拍小目标检测方法：CRP-YOLO。为提升颈部网络PANet的特征提取能力，提出一种多分支部分空洞卷积结构，将RFB模块与PConv结合改进颈部网络的特征融合方式，增大颈部网络的感受野；在主干网络SPPF层前的C2f中引入CoT（contextual Transformer）结构改进Bottleneck块，利用全局上下文信息，提升网络特征提取能力；在检测层增加一个尺寸为160×160的小目标检测头，提高对小目标的检测能力。在公开数据集VisDrone2019上进行实验，结果表明，相较于基线模型YOLOv8s，CRP-YOLO在mAP@0.5上提升3.8个百分点，mAP@0.5：0.95提升1.7个百分点，参数量降低1.5?MB，与其他主流目标检测方法相比也得到较好的检测性能。

关键词: 小目标检测, YOLOv8s, 感受野模块（RFB）, CoT

Abstract: UAV aerial target detection is a research hotspot in recent years. Due to the serious occlusion of small target images in the perspective of UAV aerial photography, problems such as missed detection and false detection occur. Aiming at the above problems, an improved YOLOv8 small target detection method in aerial photography is proposed: CRP-YOLO. Firstly, in order to improve the feature extraction ability of the neck network PANet, a multi-branch partial atrous convolution structure is proposed. The RFB module is combined with PConv to improve the feature fusion method of the neck network and increase the receptive field of the neck network. Secondly, the contextual Transformer (CoT) structure is introduced into C2f before the SPPF layer of the backbone network to improve the Bottleneck block, and the global context information is used to improve the feature extraction ability of the network. Finally, a small target detection head with the size of 160×160 is added to the detection layer to improve the detection ability of small targets. Experiments are carried out on the public dataset VisDrone2019. The results show that compared with the baseline model YOLOv8s, CRP-YOLO increases by 3.8 percentage points on mAP@0.5, 1.7 percentage points on mAP@0.5：0.95, and reduces the number of parameters by 1.5 MB. Compared with other mainstream target detection methods, it also obtains better detection performance.

Key words: small target detection, YOLOv8s, receptive field block (RFB), contextual Transformer (CoT)

赵志宏, 郝子晔. 改进YOLOv8的航拍小目标检测方法：CRP-YOLO[J]. 计算机工程与应用, 2024, 60(13): 209-218.

ZHAO Zhihong, HAO Ziye. Improved YOLOv8 Aerial Small Target Detection Method：CRP-YOLO[J]. Computer Engineering and Applications, 2024, 60(13): 209-218.

参考文献

[1] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886-893.
[2] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
[3] REN S, HE K, GIRSHICK R, et al. Faster RCNN: towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems, 2015: 91-99.
[4] 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.
[5] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 779-788.
[6] 韩玉洁, 曹杰, 刘琨, 等. 基于改进YOLO的无人机对地多目标检测[J]. 电子测量技术, 2020, 43(21): 19-24.
HAN Y J, CAO J, LIU K, et al. UAV ground multi-target detection based on improved YOLO[J]. Electronic Measurement Technology, 2020, 43(21): 19-24.
[7] 周葳楠, 吴治海, 张正道, 等. 基于弱特征增强的轻量化小目标检测方法研究[J]. 控制与决策, 2024, 39(2): 381-390.
ZHOU W N, WU Z H, ZHANG Z D, et al. Lightweight small target detection method based on weak feature enhancement[J]. Control and Decision, 2024, 39(2): 381-390.
[8] 肖粲俊, 潘睿志, 李超, 等. 基于改进YOLOv5s 绝缘子缺陷检测技术研究[J]. 电子测量技术, 2022, 45(24): 137-144.
XIAO C J, PAN R Z, LI C, et al. Research on defect detection technology based on improved YOLOv5s insulator[J]. Electronic Measurement Technology, 2022, 45(24): 137-144.
[9] 韩俊, 袁小平, 王准, 等. 基于YOLOv5s的无人机密集小目标检测算法[J]. 浙江大学学报 (工学版), 2023, 57(6): 1224-1233.
HAN J, YUAN X P, WANG Z, et al. UAV dense small target detection algorithm based onYOLOv5s[J]. Journal of Zhejiang University (Engineering Science), 2023, 57(6): 1224-1233.
[10] YANG Y Z. Drone-view object detection based on the improved YOLOv5[C]//Proceedings of the IEEE International Conference on Electrical Engineering, Big Data and Algorithms, Changchun, 2022: 612-617.
[11] 张徐, 朱正为, 郭玉英, 等. 基于cosSTR-YOLOv7的多尺度遥感小目标检测[J]. 电光与控制, 2024, 31(4): 28-34.
ZHANG X, ZHU Z W, GUO Y Y, et al. Multi-scale remote sensing small target detection based on cosSTR-YOLOv7[J]. Electronics Optics & Control, 2024, 31(4): 28-34.
[12] 崔昊杨, 杨可欣, 葛海华, 等. 面向电力开关柜的轻量型GB-YOLOv5m状态检测方法[J]. 电子与信息学报, 2022, 44(11): 3777-3787.
CUI H Y, YANG K X, GE H H, et al. Lightweight GB-YOLOv5m state detection method for power switchgear[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3777-3787.
[13] 张华卫, 张文飞, 蒋占军, 等. 引入上下文信息和Attention Gate的GUS-YOLO遥感目标检测算法[J]. 计算机科学与探索, 2024, 18(2): 453-464.
ZHANG H W, ZHANG W F, JIANG Z J, et al. GUS-YOLO remote sensing target detection algorithm introducing context information and attention gate[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(2): 453-464.
[14] 周颖, 颜毓泽, 陈海永, 等. 基于改进YOLOv8的光伏电池缺陷检测[J]. 激光与光电子学进展, 2024, 61(8): 245-255.
ZHOU Y, YAN Y Z, CHEN H Y, et al. Defect detection of photovoltaic cells based on improved YOLOv8[J]. Laser & Optoelectronics Progress, 2024, 61(8): 245-255.
[15] 王春梅, 刘欢. YOLOv8-VSC: 一种轻量级的带钢表面缺陷检测算法[J]. 计算机科学与探索, 2024, 18(1): 151-160.
WANG C M, LIU H. YOLOv8-VSC: lightweight algorithm for strip surface defect detection[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(1): 151-160.
[16] WANG G, CHEN Y F, AN P, et al. UAV-YOLOv8: a small object-detection model based on improved YOLOv8 for UAV aerial photography scenarios[J]. Sensors, 2023, 23(16): 7190.
[17] 郝帅, 赵新生, 马旭, 等. 基于TR-YOLOv5的输电线路多类缺陷目标检测方法[J]. 图学学报, 2023, 44(4): 667-676.
HAO S, ZHAO X S, MA X, et al. Multi-class defect target detection method for transmission lines based on TR-YOLOv5[J]. Journal of Graphics, 2023, 44(4): 667-676.
[18] 龙伍丹, 彭博, 胡节, 等. 基于加强特征提取的道路病害检测算法[J/OL]. 计算机应用: 1-9[2024-03-03]. http://kns.cnki.net/kcms/detail/51.1307.tp.20230925.1639.010.html.
LONG W D, PENG B, HU J, et al. Road damage detection algorithm based on enhanced feature extraction[J/OL]. Journal of Computer Applications: 1-9[2024-03-03]. http://kns.cnki.net/kcms/detail/51.1307.tp.20230925.1639.010.html.
[19] 蒋昕昊, 蔡伟, 杨志勇, 等. 基于YOLO-IDSTD算法的红外弱小目标检测[J]. 红外与激光工程, 2022, 51(3): 502-511.
JIANG X H, CAI W, YANG Z Y, et al. Infrared dim and small target detection based on YOLO-IDSTD algorithm[J]. Infrared and Laser Engineering, 2022, 51(3): 502-511.
[20] 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.
[21] LI Y, YAO T, PAN Y, et al. Contextual Transformer networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45: 1489-1500.
[22] LIU S, HUANG D, WANG Y. Receptive field block net for accurate and fast object detection[C]//Proceedings of the ECCV, 2018: 404-419.
[23] CHEN J, KAO S, HE H, et al. Run, don’t walk: chasing higher FLOPS for faster neural networks[J]. arXiv:2303.03667, 2023.
[24] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2980-2988.
[25] CAI Z, VASCONCELOS N. Cascade R-CNN: delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 6154-6162.
[26] MA S, SONG Y, CHENG N, et al. Structured light detection algorithm based on deep learning[J]. IOP Conference Series: Earth and Environmental Science, 2019, 252(4): 042050.
[27] LAW H, DENG J. CornerNet: detecting objects as paired keypoints[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 734-750.
[28] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[29] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934, 2020.
[30] LI C, LI L, JIANG H, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv:2209.
02976, 2022.
[31] 陈佳慧, 王晓虹. 改进YOLOv5的无人机航拍图像密集小目标检测算法[J]. 计算机工程与应用, 2024, 60(3): 100-108.
CHEN J H, WANG X H. Dense small object detection algorithm based on improved YOLOv5 in UAV aerial images[J]. Computer Engineering and Applications, 2024, 60(3): 100-108.
[32] ZHENG Q, ZHAO P, ZHANG D, et al. MR-DCAE: manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification[J]. International Journal of Intelligent Systems, 2021.
[33] ZHENG Q, ZHAO P, WANG H, et al. Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation[J]. IEEE Communications Letters, 2022, 26(6): 1298-1302.