Research on Optimization of UAV Traffic Small Target Image Detection Algorithm

doi:10.3778/j.issn.1002-8331.2403-0136

Abstract

Abstract: Aiming at the small target detection algorithm of UAV images with low accuracy and prone to miss and false detection, an improved target detection algorithm based on YOLOv7 is proposed. A self-constructed pedestrian and vehicle dataset is utilized from UAV aerial video. The convolution in the neck and head is replaced with the CoordConv to improve the ability to perceive the spatial information of the feature map. A small target detection layer is added to adapt to object targets at different scales and reduce the miss rate of the small targets, and the GE attention mechanism is added to the backbone network and the neck to enhance the utilization of contextual information. Wise-IoU is used as a bounding box loss function to introduce a dynamic non-monotonic focusing mechanism to improve the generalization ability of the model. The experimental results show that the improved algorithm has higher average precision than other algorithms with 91% accuracy in comparison experiments on self-built datasets. It is 2.1 percentage pionts higher than the YOLOv7 algorithm. Comparative experiments on the VisDrone2019 dataset show that the accuracy is 2.5 percentage pionts higher than that of the YOLOv7 algorithm, the comprehensive performance is better than other small-target detection algorithms, which verifies the generalization ability and effectiveness of the improved algorithm.

Key words: intelligent transportation, small target detection, deep learning, UAV images, YOLOv7 algorithm

摘要： 针对无人机图像的小目标检测算法精度低、容易出现漏检和误检的情况，提出一种基于YOLOv7的改进目标检测算法。利用无人机航拍视频自建行人和车辆数据集。将颈部和检测头中的卷积模块替换为CoordConv模块，提高算法感知特征图的空间信息能力；添加小目标检测层，适应不同尺度下的物体目标，降低小目标的漏检率；在主干网络和颈部增加GE注意力机制，加强上下文信息的利用。将Wise-IoU作为边界框损失函数，引入一种动态非单调聚焦机制，提高模型的泛化能力。实验结果表明，改进后的算法精度高于实验中其他算法，精度达到91%，比YOLOv7算法提升了2.1个百分点。在VisDrone2019数据集上进行对比实验，精度比YOLOv7算法提升了2.5个百分点；综合性能优于实验中其他小目标检测算法，验证了改进后算法的泛化能力与有效性。

关键词: 智能交通, 小目标检测, 深度学习, 无人机图像, YOLOv7算法

XU Huizhi, GU Xunan. Research on Optimization of UAV Traffic Small Target Image Detection Algorithm[J]. Computer Engineering and Applications, 2024, 60(21): 194-204.

徐慧智, 古旭楠. 无人机视角下交通小目标图像检测算法优化研究[J]. 计算机工程与应用, 2024, 60(21): 194-204.

References

[1] ZOU Z, CHEN K, SHI Z, et al. Object detection in 20 years: a survey[J]. Proceedings of the IEEE, 2023, 111(3): 257-276.
[2] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV),2015: 1440-1448.
[3] 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, 2017, 39(6): 1137-1149.
[4] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision (ECCV 2016), 2016: 21-37.
[5] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016: 779-788.
[6] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017: 6517-6525.
[7] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[8] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934, 2020.
[9] LI C, LI L, JIANG H, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv:2209.02976, 2022.
[10] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023: 7464-7475.
[11] 董刚,谢维成,黄小龙,等. 深度学习小目标检测算法综述[J]. 计算机工程与应用, 2023, 59(11): 16-27.
DONG G, XIE W C, HUANG X L, et al. Review of small object detection algorithms based on deep learning[J]. Computer Engineering and Applications, 2023, 59(11): 16-27.
[12] ZHENG Q, ZHAO P, LI Y, et al. Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification[J]. Neural Computing and Applications, 2021, 33(13): 7723-7745.
[13] ZHENG Q, TIAN X, YU Z, et al. Application of wavelet-packet transform driven deep learning method in PM2.5 concentration prediction: a case study of Qingdao, China[J]. Sustainable Cities and Society, 2023,92:104486.
[14] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), 2017: 2999-3007.
[15] LI J, LIANG X, WEI Y, et al. Perceptual generative adversarial networks for small object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 1951-1959.
[16] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768.
[17] SINGH B, DAVIS L S. An analysis of scale invariance in object detection-SNIP[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 3578-3587.
[18] LI Y, CHEN Y, WANG N, et al. Scale-aware trident networks for object detection[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019: 6053-6062.
[19] GUO C, FAN B, ZHANG Q, et al. AugFPN: improving multi-scale feature learning for object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020: 12592-12601.
[20] 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 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021: 2778-2788.
[21] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[J]. arXiv:1807.06521, 2018.
[22] LIU R, LEHMAN J, MOLINO P, et al. An intriguing failing of convolutional neural networks and the CoordConv solution[J]. arXiv:1807.03247, 2018.
[23] 张艳, 张明路, 吕晓玲, 等. 深度学习小目标检测算法研究综述[J]. 计算机工程与应用, 2022, 58(15): 1-17.
ZHANG Y, ZHANG M L, LYU X L, et al. Review of research on small target detection based on deep learning[J]. Computer Engineering and Applications, 2022, 58(15): 1-17.
[24] HU J, SHEN L, ALBANIE S, et al. Gather-excite: exploiting feature context in convolutional neural networks[J]. arXiv:1810.12348, 2018.
[25] ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2020.
[26] TONG Z, CHEN Y, XU Z, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[J]. arXiv:2301.10051, 2023.
[27] ZENG S, YANG W, JIAO Y, et al. SCA-YOLO: a new small object detection model for UAV images[J]. Visual Computer, 2023, 40: 1787-1803.
[28] 于傲泽, 魏维伟, 王平, 等. 基于分块复合注意力的无人机小目标检测算法[J]. 航空学报, 2024(14): 42-52.
YU A Z, WEI W W, WANG P, et al. Small target detection algorithm for UAV based on patch-wise co-attention[J].Acta Aeronautica et Astronautica Sinica, 2024(14): 42-52.
[29] LOU H, DUAN X, GUO J, et al. DC-YOLOv8: small-size object detection algorithm based on camera sensor[J]. Electronics, 2023, 12(10): 2323.
[30] LIANG S, WU H, ZHEN L, et al. Edge YOLO: real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 25345-25360.