基于分块复合注意力的无人机小目标检测算法

doi:10.7527/S1000-6893.2023.29148

Abstract

Abstract:

To address the problems of insufficient feature extraction and low detection accuracy of conventional target detection algorithms in the small target detection task of UAVs， a small target detection algorithm for UAVs is proposed based on Patch-Wise Co-Attention （PWCA）. Firstly， a plug-and-play PWCA is proposed. The input feature is divided into patches in spatial dimension， and channel attention weights are extracted from the patches to enhance the discrimination of channel information in terms of local spatial features， so as to improve the network granularity in small target detection scenarios. Then， the input feature and the focused feature are fused， spatial attention is further extracted， and the effective feature information in the network is paid attention to. Secondly， the step-wise convolutional downsampling in the baseline grid is abandoned， and Adaptive Interlace Downsampling （AID） is proposed in combination with PWCA to distribute the weight of the features after downsampling according to the significance of features， and to reduce the information loss of small targets during the downsampling process. Finally， the lightweight design of the backbone and feature fusion network is carried out to reduce the calculation cost. A large-scale feature map detection branch for small targets is added to optimize the flow direction of feature maps. The semantic information of feature maps in different scales is enriched and the feature expression capability is enhanced to ensure real-time performance. The Soft-NMS algorithm is used to solve the problem of missing detection when the target is occluded and overlapped to improve the performance. The effectiveness of the proposed algorithm is evaluated on the VisDrone2019 dataset. The final mAP_0.5 of the improved algorithm is 11.81% better than that of the YOLOv5s baseline algorithm， and the mAP_0.5：0.95 is 10.91% better， while the model parameters are reduced by 59%. The proposed network can effectively balance detection accuracy and inference speed for UAV small target detection tasks， demonstrating its practical significance.

Key words: UAV, YOLOv5, small target detection, attention mechanism, downsampling, feature fusion

CLC Number:

V279⁺.2

Aoze YU, Weiwei WEI, Ping WANG, Jinqiang ZHANG, Wenxiong KE. Small target detection algorithm for UAV based on patch⁃wise co⁃attention[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(14): 629148-629148.

Figures/Tables 11

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References 24

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局部块尺寸	mAP_0.5	mAP_0.5：0.95	GFLOPs
无	38.78	21.59	15.0
10×10	39.12	21.94	15.1
20×20	39.82	22.21	15.2
40×40	39.29	21.83	15.6

网络结构	mAP_0.5	mAP_0.5：0.95	GFLOPs
YOLOv5s	33.65	17.40	15.8
XYOLOv5s	38.59	21.66	19.1
XSYOLOv5s	38.78	21.59	15.0

实验编号	方法	Precision	Recall	mAP_0.5	mAP_0.5：0.95	Params/M	GFLOPs
A	YOLOv5s	44.54	34.00	33.65	17.40	7.037	15.8
B	XSYOLOv5s	48.05	38.55	38.78	21.59	2.271	15.0
C	XSYOLOv5s+CBAM	48.71	38.90	39.38	21.90	2.283	15.0
D	XSYOLOv5s+SE	48.68	38.32	38.89	21.63	2.275	15.0
E	XSYOLOv5s+CA	48.62	38.65	39.27	21.78	2.286	15.0
F	XSYOLOv5s+PWCA	48.97	39.70	39.82	22.21	2.283	15.2
G	XSYOLOv5s+AID（CBAM）	47.89	39.21	39.07	21.86	2.197	18.4
H	XSYOLOv5s+AID（PWCA）	48.82	40.36	40.58	22.91	2.879	18.9
I	H+Soft-NMS	50.23	38.92	45.46	28.31	2.879	18.9

方法	mAP_0.5	mAP_0.5：0.95	FPS
Faster R⁃CNN^［1］	22.5	15.1	15
Deformable⁃DETR^［22］	32.1	18.0	11
RetinaNet^［10］	30.1	18.5
FPN^［19］	32.2	16.5
Cascade⁃RCNN^［23］	31.9	16.1
Light⁃RCNN^［24］	32.8	16.5
YOLOv3	20.4	12.2	31
YOLOv4	32.5	16.6	35
YOLOv5s	33.7	17.4	192
Ours	40.6	22.9	181

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Small target detection algorithm for UAV based on patch⁃wise co⁃attention

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