Journal of Guangdong University of Technology ›› 2024, Vol. 41 ›› Issue (03): 71-80.doi: 10.12052/gdutxb.230044

• Computer Science and Technology • Previous Articles     Next Articles

Small Target Detection Algorithm for Lightweight UAV Aerial Photography Based on YOLOv5

Li Xue-sen1, Tan Bei-hai2, Yu Rong1, Xue Xian-bin1   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-03-04 Online:2024-05-25 Published:2024-06-14

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Abstract: A lightweight unmanned aerial vehicle (UAV) aerial photography small target detection algorithm GA-YOLO based on YOLOv5 is proposed to address the problem of small target feature size, complex background, and dense distribution in images from the perspective of UAV aerial photography. This algorithm improves the Mosaic data augmentation method and overall network structure, and adds a small object detection head. At the same time, a lightweight global attention module and a parallel spatial channel attention mechanism module are designed to enhance the network's global feature extraction ability and the competition and cooperation between convolutional channels during the training process. Based on the 4.0 version of YOLOv5s, experiments were conducted on the publicly available drone aerial photography dataset VisDrone2019-DET. The results showed that the improved model reduced the number of parameters by 48% and the computational complexity by 26% compared to the original model, and mAP@0.5 improved by 4.9 percentage points, mAP@0.5 0.95 increased by 3.3 percentage points, effectively enhancing the detection capability of unmanned aerial vehicles for dense small targets from an aerial perspective.

Key words: UAV aerial photography, YOLOv5s, small target detection, data enhancement, attention mechanism

CLC Number: 

  • TP391.41
[1] 曹家乐, 李亚利, 孙汉卿, 等. 基于深度学习的视觉目标检测技术综述[J]. 中国图象图形学报, 2022, 27(6): 1697-1722.
CAO J L, LI Y L, SUN H Q, et al. A survey on deep learning based visual object detection [J]. China Journal of Image and Graphics, 2022, 27(6): 1697-1722.
[2] 戴文君, 常天庆, 张雷, 等. 图像目标检测技术在坦克火控系统中的应用[J]. 火力与指挥控制, 2020, 45(7): 147-152.
DAI W J, CHANG T Q, ZHANG L, et al. Application of image target detection technology in tank fire control system [J]. Fire and Command Control, 2020, 45(7): 147-152.
[3] LIO W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C] //Computer Vision–ECCV 2016: 14th European Conference. Amsterdam, Netherlands: Springer International Publishing, 2016: 21-37.
[4] ZHAI S, SHANG D, WANG S, et al. DF-SSD: an improved SSD object detection algorithm based on DenseNet and feature fusion [J]. IEEE Access, 2020, 8: 24344-24357.
[5] 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. Las Vegas: IEEE, 2016: 779-788.
[6] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR) . Hawaii: IEEE, 2017: 7263-7271.
[7] REDMON J, FARHADI A. Yolov3: An incremental improvement[EB/OL]. arXiv: 1804.02767 (2018-04-08) [2023-02-07]. https://arxiv.53yu.com/abs/1804.02767.
[8] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection[EB/OL]. arXiv: 2004.10934 (2020-04-22) [2023-02-07]. https://arxiv.org/abs/2004.10934.
[9] 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. Hong Kong: IEEE, 2017: 2980-2988.
[10] GIRSHICK R. Fast R-CNN[C] //Proceedings of the IEEE International Conference on Computer Vision. Santiago Chile: IEEE, 2015: 1440-1448.
[11] 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, 2016, 39(6): 1137-1149.
[12] PURKAIT P, ZHAO C, ZACH C. SPP-Net: deep absolute pose regression with synthetic views[EB/OL]. arXiv: 1712.03452 (2017-12-09) [2023-02-09]. https://arxiv.53yu.com/abs/1712.03452.
[13] LI P, CHE C. SeMo-YOLO: a multiscale object detection network in satellite remote sensing images[C] //2021 International Joint Conference on Neural Networks (IJCNN) . Shenzhen: IEEE, 2021: 1-8.
[14] TAN L, LV X, LIAN X, et al. YOLOv4_Drone: UAV image target detection based on an improved YOLOv4 algorithm [J]. Computers & Electrical Engineering, 2021, 93: 107261.
[15] WANG M, LI Q, GU Y, et al. SCAF-net: Scene context attention-based fusion network for vehicle detection in aerial imagery [J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
[16] ZHANG X, ZHOU X, LIN M, et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices[C] //Proceedings of the IEEE Eonference on Computer Vision and Pattern Recognition. Wellington New Zealand: IEEE, 2018: 6848-6856.
[17] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C] //Proceedings of the European Conference on Computer Vision (ECCV) . Munich: EACV, 2018: 3-19.
[18] GUO M H, LU C Z, LIU Z N, et al. Visual attention network [J]. Computational Visual Media, 2023, 9(4): 733-752.
[19] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recognition at scale[EB/OL]. arXiv: 2010.11929 (2021-06-03) [2023-02-11]. https://arxiv.53yu.com/abs/2010.11929.
[20] MEHTA S, RASTEGARI M. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer[EB/OL]. arXiv: 2110.02178 (2022-03-04) [2023-02-11]. https://arxiv.53yu.com/abs/2110.02178.
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