基于超分辨率重建的小目标检测算法研究
Study on Small Target Detection Algorithm Based on Super-Resolution Reconstruction
DOI: 10.12677/MOS.2023.123297, PDF,   
作者: 苏继贤：兰州交通大学光电技术与智能控制教育部重点实验室，甘肃 兰州；兰州交通大学国家绿色镀膜技术与装备工程技术研究中心，甘肃 兰州
关键词: 目标检测；SPD-Conv；生成对抗网络；超分辨率重建；Target Detection； SPD-Conv； Generate Adversarial Networks； Super-Resolution Reconstruction
摘要: 近年来，随着深度神经网络的不断发展，使得目标检测算法对大型目标以及中型目标的检测已经具有较高的准确率，然而由于小目标在图像中面积占比较少，像素较低以及检测网络可利用特征较少等原因，导致小目标的检测存在严重的分类不准确和定位不精确的情况。为解决上述问题，本文将基于生成对抗网络的超分辨率重建技术和SPD-Conv模块融合到YOLOv5目标检测网络中。实验表明，VisDrone2019数据集上对比原始YOLOv5网络mAP@0.5提升了3.73个百分点。最后经过消融实验证明本文提出的两个模块对小目标检测效果均有一定提升。
Abstract: In recent years, with the continuous development of deep neural networks, target detection algo-rithms have achieved high accuracy in detecting large and medium-sized targets. However, due to the relatively small area of small targets in the image, low pixels, and less available features of the detection network, there are serious situations of inaccurate classification and inaccurate position-ing in the detection of small targets. In order to solve the above problems, this paper integrates the super-resolution reconstruction technology based on generating confrontation networks and the SPD-Conv module into the YOLOv5 target detection network. Experiments have shown that com-pared to the original YOLOv5 network, there is an improvement of 3.73 percent in mAP@0.5 on the VisDrone2019 dataset. Finally, the ablation experiment proves that the three modules proposed in this paper have a certain improvement in the detection effect of small targets.
文章引用：苏继贤. 基于超分辨率重建的小目标检测算法研究[J]. 建模与仿真, 2023, 12(3): 3224-3237. https://doi.org/10.12677/MOS.2023.123297

参考文献

[1] Dalal, N. and Triggs, B. (2005) Histograms of Oriented Gradients for Human Detection. 2005 IEEE Computer Society Confer-ence on Computer Vision and Pattern Recognition (CVPR’05), San Diego, 20-25 June 2005, 886-893. [Google Scholar] [CrossRef] 
[2] Szegedy, C., Toshev, A. and Erhan, D. (2013) Deep Neural Networks for Object Detection. In: Burges, C.J., Bottou, L., Welling, M., Ghahramani, Z. and Weinberger, K.Q., Eds., Advances in Neural Information Processing Systems 26 (NIPS 2013), Curran Associates Inc., Red Hook.
[3] Ding, S. and Zhao, K. (2018) Re-search on Daily Objects Detection Based on Deep Neural Network. IOP Conference Series: Materials Science and Engineering, 322, Article ID: 062024. [Google Scholar] [CrossRef] 
[4] Zhang, N., Donahue, J., Girshick, R. and Darrell, T. (2014) Part-Based R-CNNs for Fine-Grained Category Detection. In: Fleet, D., Pajdla, T., Schiele, B. and Tuytelaars, T., Eds., Computer Vision—ECCV 2014. Lecture Notes in Computer Science, Vol. 8689, Springer, Cham, 834-849. [Google Scholar] [CrossRef] 
[5] Ren, S., He, K., Girshick, R. and Sun, J. (2015) Faster R-CNN: To-wards Real-Time Object Detection with Region Proposal Networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M. and Garnett, R., Eds., Advances in Neural Information Processing Systems 28 (NIPS 2015), Curran Associates Inc., Red Hook.
[6] Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016) You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 779-788. [Google Scholar] [CrossRef] 
[7] Liu, W., Anguelov, D., Erhan, D., et al. (2016) SSD: Single Shot MultiBox Detector. In: Leibe, B., Matas, J., Sebe, N. and Welling, M., Eds., Computer Vision—ECCV 2016. Lecture Notes in Computer Science, Vol. 9905, Springer, Cham, 21-37. [Google Scholar] [CrossRef] 
[8] Creswell, A., White, T., Dumoulin, V., et al. (2018) Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine, 35, 53-65. [Google Scholar] [CrossRef] 
[9] Bai, Y., Zhang, Y., Ding, M. and Ghanem, B. (2018) SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network. In: Ferrari, V., Hebert, M., Sminchisescu, C. and Weiss, Y., Eds., Computer Vision—ECCV 2018. Lecture Notes in Computer Science, Vol. 11217, Springer, Cham, 206-221. [Google Scholar] [CrossRef] 
[10] Rabbi, J., Ray, N., Schubert, M., Chowdhury, S. and Chao, D. (2020) Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Re-mote Sensing, 12, Article No. 1432. [Google Scholar] [CrossRef] 
[11] Wang, X., Yu, K., Wu, S., et al. (2019) ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. In: Leal-Taixé, L. and Roth, S., Eds., Computer Vision—ECCV 2018 Workshops. Lecture Notes in Computer Science, Vol. 11133, Springer, Cham, 63-79. [Google Scholar] [CrossRef] 
[12] Ledig, C., Theis, L., Huszár, F., et al. (2017) Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 105-114. [Google Scholar] [CrossRef] 
[13] Chen, Y., Li, J., Xiao, H., et al. (2017) Dual Path Networks. Advances in Neural Information Processing Systems. In: Guyon, I., et al., Eds., Advances in Neural Information Processing Systems 30 (NIPS 2017), Curran Associates Inc., Red Hook.
[14] Martin, D., Fowlkes, C., Tal, D. and Malik, J. (2001) A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statis-tics. Proceedings 8th IEEE International Conference on Computer Vision. ICCV 2001, Vancouver, 7-14 July 2001, 416-423.
[15] Sunkara, R. and Luo, T. (2023) No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P. and Tsoumakas, G., Eds., Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science, Vol. 13715, Springer, Cham, 443-459. [Google Scholar] [CrossRef] 
[16] Zhu, P., Wen, L., Du, D., et al. (2021) Detection and Tracking Meet Drones Challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 7380-7399. [Google Scholar] [CrossRef] 

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