Small UVA Target Detection Algorithm Based on Multi-scale Attention Mechanism

doi:10.12382/bgxb.2023.1124

Abstract

Abstract:

A SBE_YOLOv8s small target detection algorithm based on multi-scale attention mechanism is proposed for the UAV aerial images with high density,small target size and complex background.First,a feature extraction module EF_C2f(EMA-Faster Block_C2f) based on the multi-scale attention mechanism is designed to replace the C2f module in the YOLOv8 network to improve the network’s ability to extract small target features.And then a P1 detection layer is added to the feature fusion network,and a cross-scale feature fusion structure BPAN(Bi-Path Aggregation Network) is designed to fuse the small target feature information.Finally,a tiny target detection head is added,and SIoU Loss is used as the bounding-box loss function to improve the detection accuracy of small targets and the convergence speed of the network.The proposed algorithm is validated on the public dataset VisDrone2019.Compared with YOLOv8s algorithm,the proposed algorithm improves the detection accuracy by 6.9% and mAP₅₀ by 9.1%,and reduces the amount of parameters of the model is by 46.4%,and the detection speed is 28 fps.The experimental results show that the proposed algorithm has a certain degree of utility in the field of small target detection.

Key words: multi-scale attention mechanism, YOLOv8s algorithm, feature extraction, cross-scale feature fusion, small target detection

CLC Number:

TP391

FENG Yingbin, GUO Xiaozun, YAN Jiahua. Small UVA Target Detection Algorithm Based on Multi-scale Attention Mechanism[J]. Acta Armamentarii, 2025, 46(1): 231124-.

Figures/Tables 15

References 29

[1]	İBROŞKA B, ÖZPEYNIRCI S, ÖZPEYNIRCI Ö. Multiple traveling salesperson problem with drones:general variable neighborhood search approach[J]. Computers and Operations Research, 2023,160:106390.
[2]	HU X, ASSAAD R H. The use of unmanned ground vehicles(mobile robots) and unmanned aerial vehicles(drones) in the civil infrastructure asset management sector:applications,robotic platforms,sensors,and algorithms[J]. Expert Systems with Applications, 2023,232:120897.
[3]	LI S J, WANG L Z, LI J, et al. Image classification algorithm based on improved AlexNet[J]. Journal of Physics:Conference Series. IOP Publishing, 2021, 1813(1):012051.
[4]	惠康华, 杨卫, 刘浩翰, 等. 基于YOLOv5的增强多尺度目标检测方法[J]. 兵工学报, 2023, 44(9):2600-2610. doi: 10.12382/bgxb.2022.1147
	HUI K H, YANG W, LIU H H, et al. Enhanced multi-scale target detection method based on YOLOv5[J]. Acta Armamentarii, 2023, 44(9):2600-2610. (in Chinese) doi: 10.12382/bgxb.2022.1147
[5]	GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision.Santiago, Chile:IEEE,2015:1440-1448.
[6]	REN S Q, HE K M, GIRSHICK R B, et al. Faster R-CNN:towards real-time object detection with region proposal networks[C] //Proceedings of the International Conference on Neural Information Processing System.Cambridge,MA, US: MIT Press,2015:91-99.
[7]	CAI Z W, VASCONCELOS N. Cascade R-CNN:high quality object detection and instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(5):1483-1498.
[8]	MITTAL P, SHARMA A, SINGH R. A feature pyramid based multi-stage framework for object detection in low-altitude UAV images[J]. International Journal on Artificial Intelligence Tools, 2022, 31(2):2250028.
[9]	王殿伟, 胡里晨, 房杰, 等. 基于改进Double-Head R-CNN的无人机航拍图像小目标检测算法[J]. 北京航空航天大学学报, 2024, 50(7):2141-2149.
	WANG D W, HU L C, FANG J, et al. Small object detection algorithm based on improved Double-Head RCNN for UAV aerial images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024, 50(7):2141-2149. (in Chinese)
[10]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,D.C., US:IEEE,2016:779-788.
[11]	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: Springer,2016:21-37.
[12]	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.
[13]	KIM M, JEONG J, KIM S. ECAP-YOLO:efficient channel attention pyramid YOLO for small object detection in aerial image[J]. Remote Sensing, 2021, 13(23):4851.
[14]	NIE H J, PANG H L, MA M Y, et al. A Lightweight remote sensing small target image detection algorithm based on improved YOLOv8[J]. Sensors, 2024, 24(9):2952.
[15]	WOO S, PARK J, LEE J Y, et al. CBAM:convolutional block attention module[C]//Proceedings of the 15th European Conference on Computer Vision.Munich, Germany:Springer,2018:3-19.
[16]	ZHANG Q L, YANG Y B. SA-Net:shuffle attention for deep convolutional neural networks[C]//Proceedings of 2021 IEEE International Conference on Acoustics,Speech and Signal Processing.Toronto,ON, Canada:IEEE,2021:2235-2239.
[17]	OUYANG D, HE S, ZHANG G, et al. Efficient multi-scale attention module with cross-spatial learning[C]//Proceedings of 2023 IEEE International Conference on Acoustics,Speech and Signal Processing.Rhodes Island, Greece:IEEE,2023:1-5.
[18]	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 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Vancouver,BC, Canada:IEEE,2023:12021-12031.
[19]	ZHU P F, WEN L Y, DU D W, et al. Detection and tracking meet drones challenge[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(11):7380-7399.
[20]	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. doi: 10.12263/DZXB.20220313
	ZHANG Z, YI H H, ZHENG J. Focusing on small objects detector in aerial images[J]. Acta Electronica Sinica, 2023, 51(4):944-955. (in Chinese) doi: 10.12263/DZXB.20220313
[21]	WANG K X, LIEW J H, ZOU Y T, et al. PANet:few-shot image semantic segmentation with prototype alignment[C]// Proceedings of the IEEE/CVF International Conference on Computer Vision.Seoul, Korea:IEEE,2019:9197-9206.
[22]	TAN M X, PANG R M, LE Q V. EfficientDet:scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Seattle,WA, US:IEEE,2020:10781-10790.
[23]	GEVORGYAN Z. SIoU loss:more powerful learning for bounding box regression[R/OL]. Ithaca,NY, US: Cornell University, 2022.
[24]	LI X, WANG W H, WU L J, et al. Generalized focal loss:learning qualified and distributed bounding boxes for dense object detection[J]. Advances in Neural Information Processing Systems, 2020,33:21002-21012.
[25]	冒国韬, 邓天民, 于楠晶. 基于多尺度分割注意力的无人机航拍图像目标检测算法[J]. 航空学报, 2023, 44(5):326738. doi: 10.7527/S1000-6893.2021.26738
	MAO G T, DENG T M, YU N J. Measurement of the engraving process of projectiles[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(5):326738. (in Chinese)
[26]	李校林, 刘大东, 刘鑫满, 等. 改进YOLOv5的无人机航拍图像目标检测算法[J]. 计算机工程与应用, 2024, 60(11):204-214. doi: 10.3778/j.issn.1002-8331.2307-0171
	LI X L, LIU D D, LIU X M, et al. Target detection algorithm of UAV aerial image based on improved YOLOv5[J]. Computer Engineering and Applications, 2024, 60(11):204-214. (in Chinese) doi: 10.3778/j.issn.1002-8331.2307-0171
[27]	LIU H Y, SUN F Q, GU J, et al. SF-YOLOv5:a lightweight small object detection algorithm based on improved feature fusion mode[J]. Sensors, 2022, 22(15):5817.
[28]	于博文, 吕明. 改进的YOLOv3算法及其在军事目标检测中的应用[J]. 兵工学报, 2022, 43(2):345-354. doi: 10.3969/j.issn.1000-1093.2022.02.012
	YU B W, LÜ M. Improved YOLOv3 algorithm and its application in military target detection[J]. Acta Armamentarii, 2022, 43(2):345-354. (in Chinese) doi: 10.3969/j.issn.1000-1093.2022.02.012
[29]	奉志强, 谢志军, 包正伟, 等. 基于改进YOLOv5的无人机实时密集小目标检测算法[J]. 航空学报, 2023, 44(7):327106. doi: 10.7527/S1000-6893.2022.27106
	FENG Z Q, XIE Z J, BAO Z W, et al. Real-time dense small object detection algorithm for UAV based on improved YOLOv5[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(7):327106. (in Chinese)

配置	版本
操作系统	Windows 10
处理器	Intel Xeon Sliver 4210 CPU @2.20GHz
显卡	NVIDIA Quadro RTX 4000
框架	Pytorch 1.13.1
开发软件	Pycharm 2020.3.12
开发语言	Python 3.8.17

配置	版本
操作系统	Windows 10
处理器	Intel Xeon Sliver 4210 CPU @2.20GHz
显卡	NVIDIA Quadro RTX 4000
框架	Pytorch 1.13.1
开发软件	Pycharm 2020.3.12
开发语言	Python 3.8.17

编号	模型	P/%	mAP₅₀/%	mAP_50-95/%	Par/10⁶	O/10⁹
1	YOLOv8s	44.8	33.0	18.6	11.2	28.5
2	YOLOv8s+BPAN	49.4	40.2	23.1	7.7	123.4
3	YOLOv8s+BPAN+SIoU Loss	50.1	40.9	23.4	7.7	123.4
4	YOLOv8s+BPAN+EF_C2f	51.2	41.8	23.8	6.2	105.4
5	YOLOv8s+BPAN+SIoU Loss+EF_C2f	51.7	42.1	24.3	6.2	105.4

编号	模型	P/%	mAP₅₀/%	mAP_50-95/%	Par/10⁶	O/10⁹
1	YOLOv8s	44.8	33.0	18.6	11.2	28.5
2	YOLOv8s+BPAN	49.4	40.2	23.1	7.7	123.4
3	YOLOv8s+BPAN+SIoU Loss	50.1	40.9	23.4	7.7	123.4
4	YOLOv8s+BPAN+EF_C2f	51.2	41.8	23.8	6.2	105.4
5	YOLOv8s+BPAN+SIoU Loss+EF_C2f	51.7	42.1	24.3	6.2	105.4

方法	mAP₅₀/%	mAP_50-95/%	FPS
Faster-RCNN	21.7	15.1	15
Cascade-RCNN	31.9	16.1
Light-RCNN	30.8	16.5
YOLOv3	32.1	17.5	31
YOLOv4	30.7	15.9	32
YOLOv5	31.5	16.8	121
YOLOv8	33.0	18.6	169
Ours	42.1	24.3	28