Small Object Detection Algorithm Based on Improved YOLOv5 in UAV Image

被引：9

作者：

Xie, Chunhui ^{[1
,2
]}

Wu, Jinming ^{[1
]}

Xu, Huaiyu ^{[2
]}

机构：

[1] Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai

[2] School of Information Science and Technology, ShanghaiTech University, Shanghai

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 09期

关键词：

attention mechanism; feature fusion; object detection; small object; unmanned aerial vehicle（UAV）; YOLO;

D O I：

10.3778/j.issn.1002-8331.2212-0336

中图分类号：

学科分类号：

摘要：

UAV aerial images have many characteristics, such as large-scale changes and complex backgrounds, so it is difficult for the existing detectors to detect small objects in aerial images. Aiming at the problem of mistake detection and omission, a small object detection algorithm model Drone-YOLO is proposed. A new detection branch is added to improve the detection capability at multiple scales, meanwhile the model contains a novel feature pyramid network with multi-level information aggregation, which realizes the fusion of cross-layers information. Then a feature fusion module based on multi-scale channel attention mechanism is designed to improve the focus on small objects. The classification task of the prediction head is decoupled from the regression task, and the loss function is optimized using Alpha-IoU to improve the accuracy of detection. The experimental results of VisDrone dataset show that the Drone-YOLO has improved the AP50 by 4.91 percentage points compared with the YOLOv5, and the inference time is only 16.78 ms. Compared with other mainstream models, it has a better detection effect for small targets, and can effectively complete the task of small target detection in UAV aerial images. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：198 / 206

页数：8

共 25 条

[1]

HE J, MA X, Et al., Alpha-IoU：a family of power intersection over union losses for bounding box regression[C], Advances in Neural Information Processing Systems, pp. 20230-20242, (2021)

[2]

KISANTAL M, WOJNA Z, MURAWSKI J, Et al., Augmentation for small object detection[J], (2019)

[3]

CHEN C, ZHANG Y, LV Q, Et al., RRNet：a hybrid detector for object detection in drone-captured images[C], Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 100-108, (2019)

[4]

YU X, GONG Y, JIANG N, Et al., Scale match for tiny person detection[C], Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1257-1265, (2020)

[5]

CHEN Y, ZHANG P, LI Z，, Et al., Stitcher：feedback-driven data provider for object detection[J], (2020)

[6]

LIN T Y, DOLLAR P, Et al., Feature pyramid networks for object detection[C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117-2125, (2017)

[7]

DENG C, WANG M, LIU L, Et al., Extended feature pyramid network for small object detection[J], IEEE Transactions on Multimedia, 24, pp. 1968-1979, (2021)

[8]

LI Q Y, DENG Z H, LUO X Q, Et al., SSD object detection algorithm with attention and cross- scale fusion[J], Journal of Frontiers of Computer Science and Technology, 16, 11, pp. 2575-2586, (2022)

[9]

LIANG Y Y, LI J B., Small objects detection method based on multi-scale non-local attention network[J], Journal of Frontiers of Computer Science and Technology, 14, 10, pp. 1744-1753, (2020)

[10]

ZHU X, WANG X, Et al., TPH-YOLOv5：improved YOLOv5 based on transformer prediction head for object detection on drone- captured scenarios[C], Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2778-2788, (2021)

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