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| Small target detection algorithm for aerial images based on feature reuse mechanism |
| Tianmin DENG,Xinxin CHENG,Jinfeng LIU,Xiyue ZHANG |
| 1. School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China |
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Abstract A lightweight and efficient aerial image detection algorithm called Functional ShuffleNet YOLO (FS-YOLO) was proposed based on YOLOv8s, in order to address the issues of low detection accuracy for small targets and a large number of model parameters in current unmanned aerial vehicle (UAV) aerial image detection. A lightweight feature extraction network was introduced by reducing channel dimensions and improving the network architecture. This facilitated the efficient reuse of redundant feature information, generating more feature maps with fewer parameters, enhancing the model’s ability to extract and express feature information while significantly reducing the model size. Additionally, a content-aware feature recombination module was introduced during the feature fusion stage to enhance the attention on salient semantic information of small targets, thereby improving the detection performance of the network for aerial images. Experimental validation was conducted using the VisDrone dataset, and the results indicated that the proposed algorithm achieved a detection accuracy of 47.0% mAP0.5 with only 5.48 million parameters. This represented a 50.7% reduction in parameter count compared to the YOLOv8s benchmark algorithm, along with a 6.1% improvement in accuracy. Experimental results of DIOR dataset showed that FS-YOLO had strong generalization and was more competitive than other state-of-the-art algorithms.
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Received: 20 May 2023
Published: 05 March 2024
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| Fund: 国家重点研发计划资助项目（2022YFC3800502）；重庆市技术创新与应用发展专项重点资助项目（cstc2021jscx-gksbX0058，CSTB2022TIAD-KPX0113，CSTB2022TIAD-KPX0118）. |
基于特征复用机制的航拍图像小目标检测算法
针对无人机(UAV)航拍图像检测存在的小目标检测精度低和模型参数量大的问题，提出轻量高效的航拍图像检测算法FS-YOLO. 该算法以YOLOv8s为基准网络，通过降低通道维数和改进网络架构提出轻量的特征提取网络，实现对冗余特征信息的高效复用，在较少的参数量下产生更多特征图，提高模型对特征信息的提取和表达能力，同时显著减小模型大小. 在特征融合阶段引入内容感知特征重组模块，加强对小目标显著语义信息的关注，提升网络对航拍图像的检测性能. 使用无人机航拍数据集VisDrone进行实验验证，结果表明，所提算法以仅5.48 M的参数量实现了mAP0.5=47.0%的检测精度，比基准算法YOLOv8s的参数量降低了50.7%，精度提升了6.1%. 在DIOR数据集上的实验表明，FS-YOLO的泛化能力较强，较其他先进算法更具竞争力.
关键词：
无人机(UVA)图像,
目标检测,
YOLOv8,
轻量化主干,
CARAFE
|
|
| [1] |
ZHANG X, WANG C, JIN J, et al Object detection of VisDrone by stronger feature extraction FasterRCNN[J]. Journal of Electronic Imaging, 2023, 32 (1): 13- 18
|
|
|
| [2] |
HUANG H, LI L, MA H. An improved Cascade R-CNN based target detection algorithm for UAV aerial images [C]// 2022 7th International Conference on Image, Vision and Computing . Xi’an: IEEE, 2022: 232−237.
|
|
|
| [3] |
ZHU X, LYU S, 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 Workshops . Montreal: IEEE, 2021: 2778−2788.
|
|
|
| [4] |
LIU C, YANG D, TANG L, et al A lightweight object detector based on spatial-coordinate self-attention for UAV aerial images[J]. Remote Sensing, 2023, 15 (1): 83
|
|
|
| [5] |
HOWARD A, ZHU M, CHEN B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications [EB/OL]. (2017-04-17) [2023-05-04]. https://arxiv.org/abs/1704.04861.
|
|
|
| [6] |
ZHANG X, ZHOU X, LIN M, et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City: IEEE, 2018: 6848–6856.
|
|
|
| [7] |
MA N, ZHANG X, ZHENG H T, et al. Shufflenet v2: practical guidelines for efficient CNN architecture sign [C]// Proceedings of the European Conference on Computer Vision . Munich: Springer, 2018: 116–131.
|
|
|
| [8] |
YU G, CHANG Q, LV W, et al. PP-PicoDet: a bett-er real-time object detector on mobile devices [EB/OL]. (2021-12-01) [2023-05-04]. https://arxiv.org/abs/2111.00902.
|
|
|
| [9] |
WANG J, CHEN K, XU R, et al. CARAFE: content-aware reassembly of features [C]// 2019 IEEE/CVF International Conference on Computer Vision . Seoul: IEEE, 2019: 3007−3016.
|
|
|
| [10] |
JOCHER G, NISHIMURA K, MINEEVA T, et al. YOLOv8 [EB/OL]. (2023-01-13) [2023-05-04]. https://github.com/ultralytics/ultralytics/tags.
|
|
|
| [11] |
JOCHER G, NISHIMURA K, MINEEVA T, et al. YOLOv5 (minor version 6.2) [EB/OL]. (2022-04-17) [2023-05-04]. https://github.com/ultralytics/yolov5/releases/tag/v6.2.
|
|
|
| [12] |
张融, 张为 基于改进GhostNet-FCOS的火灾检测算法[J]. 浙江大学学报:工学版, 2022, 56 (10): 1891- 1899
ZHANG Rong, ZHANG Wei Fire detection algorithm based on improved GhostNet-FCOS[J]. Journal of Zhejiang University: Engineering Science, 2022, 56 (10): 1891- 1899
|
|
|
| [13] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City: IEEE, 2018: 7132-7141.
|
|
|
| [14] |
HAN K, WANG Y, TIAN Q, et al. Ghostnet: more features from cheap operations [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle: IEEE, 2020: 1580−1589.
|
|
|
| [15] |
HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . [s.l.]: IEEE, 2021: 13713−13722.
|
|
|
| [16] |
CHEN J, KAO S, HE H, et al. Run, don't walk: chasing higher FLOPS for faster neural networks [EB/OL]. (2022-04-04) [2023-05-04]. https://arxiv.org/abs/2303.03667.
|
|
|
| [17] |
DU D, ZHU P, WEN L, et al. VisDrone-DET2019: the vision meets drone object detection in image challenge results [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Seoul: IEEE, 2019: 213−226.
|
|
|
| [18] |
YU W, YANG T, CHEN C. Towards resolving the challenge of long-tail distribution in UAV images for object detection [C]// Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision . Waikoloa: IEEE, 2021: 3257−3266.
|
|
|
| [19] |
ALI S, SIDDIQUE A, ATES H, et al. Improved YOLOv4 for aerial object detection [C]// Proceedings of the 2021 29th Signal Processing and Communications Applications Conference . Istanbul: IEEE, 2021, 1−4.
|
|
|
| [20] |
YANG Y, GAO X, WANG Y, et al VAMYOLOX: an accurate and efficient object detection algorithm based on visual attention mechanism for UAV optical sensors[J]. IEEE Sensors Journal, 2023, 23 (11): 11139- 11155
doi: 10.1109/JSEN.2022.3219199
|
|
|
| [21] |
WANG C, BOCHKOVSKIY A, LIAO H M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver: IEEE, 2023: 7464−7475.
|
|
|
| [22] |
LI K, WAN G, CHENG G, et al Object detection in optical remote sensing images: a survey and a new benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159: 296- 307
doi: 10.1016/j.isprsjprs.2019.11.023
|
|
|
| [23] |
WANG, G, ZHUANG, Y, CHEN, et al FSoD-Net: full-scale object detection from optical remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1- 18
|
|
|
| [24] |
YANG X, YAN J, LIAO W, et al Scrdet++: detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45 (2): 2384- 2399
|
|
|
| [25] |
LIU Y, LI H, HU C, et al. Catnet: context aggregate on network for instance segmentation in remote sensing images [EB/OL]. (2022-05-27) [2023-05-04]. https://arxiv.org/abs/2111.11057.
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