Remote Sensing Image Object Detection Based on Improved MoCo

Abstract

Abstract: In the intelligent processing of satellite remote sensing images, there are some problems such as inconsistent standards and uneven data distribution, resulting in few effective samples and poor object detection effect. Aiming at this phenomenon, an object detection algorithm based on MoCo unsupervised contrast learning model is proposed. The framework of object detection adopts YOLOv5 with ResNet50 as the backbone network, and the weight of ResNet50 obtained by contrastive learning is used as a fixed value to participate in the detection task training of YOLOv5 downstream without gradient iteration. The contrastive learning experiment is carried out on AID Dataset, and the top-1 accuracy of the improved MoCo v2 is 95.888%. In the downstream detection task, using the TGRS-HRRSD Dataset, the accuracy of mAP@.5:.95 with the improved MoCo v2 pre-training weight is 67.8%, which is 5.6 percentage points higher than that without the pre-training weight. The results show that the improved MoCo comparative learning model is effective, and the detection accuracy is also improved in the downstream detection tasks after the comparative learning.

Key words: unsupervised contrastive learning, remote sensing image detection, attention, YOLOv5

JIAO Xin-quan, LI Rui-kang, CHEN Jian-jun. Remote Sensing Image Object Detection Based on Improved MoCo[J]. Computer and Modernization, 2022, 0(12): 88-94.

References

［1］ ZHANG X, LIU L Y, CHEN X D, et al. A novel multitemporal cloud and cloud shadow detection method using the integrated cloud z-scores model［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019,12(1):123-134.
［2］ GUO J H, YANG J Y, YUE H J, et al. CDnetV2: CNN-based cloud detection for remote sensing imagery with cloud-snow coexistence［J］. IEEE Transactions on Geoscience and Remote Sensing, 2021,59(1):700-713.
［3］ HU J, SHEN L, SUN G. Squeeze-and-excitation networks［C］// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:7132-7141.
［4］ WANG Q, WU B, ZHU P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020:11531-11539.
［5］ XIA G S, HU J W, HU F, et al. AID: A benchmark data set for performance evaluation of aerial scene classification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2017,55(7):3965-3981.
［6］ ZHANG Y L, YUAN Y, FENG Y C, et al. Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2019,57(8):5535-5548.
［7］ CHEN J, WAN L, ZHU J R, et al. Multi-scale spatial and channel-wise attention for improving object detection in remote sensing imagery［J］. IEEE Geoscience and Remote Sensing Letters, 2020,17(4):681-685.
［8］ TAN Q L, LING J, HU J, et al. Vehicle detection in high resolution satellite remote sensing images based on deep learning［J］. IEEE Access, 2020,8:153394-153402.
［9］ GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］// 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014:580-587.
［10］REN S Q, HE K M, 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.
［11］REDMON J, FARHADI A. YOLOv3: An incremental improvement［J］. arXiv preprint arXiv:1804.02767, 2018.
［12］BOCHKOVSKIY A, WANG C Y, MARK LIAO H Y. YOLOv4: Optimal speed and accuracy of object detection［J］. arXiv preprint arXiv:2004.10934, 2020.
［13］LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single ShotMultiBox Detector［C］// 2016 European Conference on Computer Vision. 2016:21-37.
［14］FU C Y, LIU W, RANGA A, et al. DSSD: Deconvolutional single shot detector［J］. arXiv preprint arXiv:1701.06659, 2017.
［15］HE K M, ZHANG X, REN S Q, et al. Deep residual learning for image recognition［C］// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:770-778.
［16］LIN T, DOLLR P, GIRSHICK R, et al. Feature pyramid networks for object detection［C］// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:936-944.
［17］LI H, XIONG P, AN J, et al. Pyramid attention network for semantic segmentation［J］. arXiv preprint arXiv:1805.10180, 2018.
［18］DOSOVITSKIY A, FISCHER P, SPRINGENBERG J T, et al. Discriminative unsupervised feature learning with exemplar convolutional neural networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016,38(9):1734-1747.
［19］YE M, ZHANG X, YUEN P C, et al. Unsupervised embedding learning via invariant and spreading instance feature［C］// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019:6203-6212. 〖HJ0.27mm〗
［20］OORD A V D, LI Y, VINYALS O. Representation learning with contrastive predictive coding［J］. arXiv preprint arXiv:1807.03748, 2018.
［21］HNAFF O J, SRINIVAS A, FAUW J D, et al. Data-efficient image recognition with contrastive predictive coding［C］// Proceedings of the 37th International Conference on Machine Learning. 2020:4182-4192.
［22］DEVON HJELM R, FEDOROV A, LAVOIE-MARCHILDON S, et al. Learning deep representations by mutual information estimation and maximization［J］. arXiv preprint arXiv:1808.06670, 2018.
［23］BACHMAN P, HJELM R D, BUCHWALTER W. Learning representations by maximizing mutual information across views［C］// Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019:15535-15545.
［24］WU Z R, XIONG Y J, YU S X, et al. Unsupervised feature learning via non-parametric instance discrimination［C］// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:3733-3742.
［25］HE K M, FAN H Q, WU Y X, et al. Momentum contrast for unsupervised visual representation learning［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020:9726-9735.
［26］CHEN X L, FAN H Q, GIRSHICK R, et al. Improved baselines with momentum contrastive learning［J］. arXiv preprint arXiv:2003.04297, 2020.
［27］CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations［J］. arXiv preprint arXiv:2002.05709, 2020.
［28］XIE E, DING J, WANG W, et al.DetCo: Unsupervised contrastive learning for object detection［C］// 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2021:8372-8381.
［29］LI Y, ZHANG Y, HUANG X, et al. Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2018,146:182-196.
［30］LI Y, CHEN W, ZHANG Y, et al. Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning［J］. Remote Sensing of Environment, 2020. DOI:10.1016/j.rse.2020.112045.
［31］LI Y, KONG D, ZHANG Y, et al. Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2021,179:145-158.
［32］GUTMANN M, HYVRINEN A. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models［J］. Journal of Machine Learning Research, 2010,9:297-304.
［33］CARON M, BOJANOWSKI P, JOULIN A, et al. Deepclustering for unsupervised learning of visual features［C］// 2018 European Conference on Computer Vision. 2018:139-156.
［34］CARON M, MISRA I, MAIRAL J, et al. Unsupervised learning of visual features by contrasting cluster assignments［C］// Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020:9912-9924.
［35］JIANG Z, VON N K, LOISEL J, et al.ArcticNet: A deep learning solution to classify arctic wetlands［J］. arXiv preprint arXiv:1906.00133, 2019.
［36］LU X, ZHANG Y, YUAN Y, et al. Gated and axis-concentrated localization network for remote sensing object detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2020,58(1):179-192.
［37］韩伟. 基于深度神经网络的高分辨率遥感影像弱小目标检测［D］. 武汉:中国地质大学, 2021.

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