• Laser & Optoelectronics Progress
  • Vol. 60, Issue 12, 1228010 (2023)
Luobing Wu1, Yuhai Gu1,2,*, Wenhao Wu1, and Shuaixin Fan1
Author Affiliations
  • 1Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100089, China
  • 2Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100089, China
  • show less
    DOI: 10.3788/LOP221716 Cite this Article Set citation alerts
    Luobing Wu, Yuhai Gu, Wenhao Wu, Shuaixin Fan. Remote Sensing Rotating Object Detection Based on Multi-Scale Feature Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228010 Copy Citation Text show less
    References

    [1] Li X D, Ye M, Li T. Review of object detection based on convolutional neural networks[J]. Application Research of Computers, 34, 2881-2886, 2891(2017).

    [2] Jiang Y Y, Zhu X Y, Wang X B et al. R2CNN: rotational region CNN for orientation robust scene text detection[EB/OL]. https://arxiv.org/abs/1706.09579

    [3] Ma J Q, Shao W Y, Ye H et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE Transactions on Multimedia, 20, 3111-3122(2018).

    [4] Yang X, Yang J R, Yan J C et al. SCRDet: towards more robust detection for small, cluttered and rotated objects[C], 8231-8240(2019).

    [5] Yang X, Yan J, Feng Z et al. R3det: refined single-stage detector with feature refinement for rotating object[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 3163-3171(2021).

    [6] Qian W, Yang X, Peng S L et al. Learning modulated loss for rotated object detection[EB/OL]. https://arxiv.org/abs/1911.08299

    [7] Xu Y C, Fu M T, Wang Q M et al. Gliding vertex on the horizontal bounding box for multi-oriented object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 1452-1459(2021).

    [8] Uijlings J R R, van de Sande K E A, Gevers T et al. Selective search for object recognition[J]. International Journal of Computer Vision, 104, 154-171(2013).

    [9] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[EB/OL]. https://arxiv.org/abs/1512.02325

    [10] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

    [11] Ding J, Xue N, Long Y et al. Learning RoI transformer for oriented object detection in aerial images[C], 2844-2853(2019).

    [12] Ma T L, Mao M Y, Zheng H H et al. Oriented object detection with transformer[EB/OL]. https://arxiv.org/abs/2106.03146

    [13] Law H, Deng J. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 128, 642-656(2020).

    [14] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points[EB/OL]. https://arxiv.org/abs/1904.07850

    [15] Zhang F, Wang X Y, Zhou S L et al. Arbitrary-oriented ship detection through center-head point extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5612414(2022).

    [16] Yi J R, Wu P X, Liu B et al. Oriented object detection in aerial images with box boundary-aware vectors[EB/OL]. https://arxiv.org/abs/2008.07043

    [17] 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, 39, 1137-1149(2017).

    [18] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [19] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[EB/OL]. https://arxiv.org/abs/1511.07122

    [20] Luo W J, Li Y J, Urtasun R et al. Understanding the effective receptive field in deep convolutional neural networks[C], 4905-4913(2016).

    [21] Liu S T, Huang D, Wang Y H. Learning spatial fusion for single-shot object detection[EB/OL]. https://arxiv.org/abs/1911.09516

    [22] Neubeck A, Van Gool L. Efficient non-maximum suppression[C], 850-855(2006).

    [23] Yang X, Yan J C. Arbitrary-oriented object detection with circular smooth label[EB/OL]. https://arxiv.org/abs/2003.05597

    [24] Xia G S, Bai X, Ding J et al. DOTA: a large-scale dataset for object detection in aerial images[C], 3974-3983(2018).

    [25] MacQueen J. Some methods for classification and analysis of multivariate observations[EB/OL]. http://www.cs.cmu.edu/~bhiksha/courses/mlsp.fall2010/class14/macqueen.pdf

    [26] Zhu H G, Chen X G, Dai W Q et al. Orientation robust object detection in aerial images using deep convolutional neural network[C], 3735-3739(2015).

    [27] Loshchilov I, Hutter F. SGDR: stochastic gradient descent with warm restarts[EB/OL]. https://arxiv.org/abs/1608.03983

    [28] Azimi S M, Vig E, Bahmanyar R et al. Towards multi-class object detection in unconstrained remote sensing imagery[EB/OL]. https://arxiv.org/abs/1807.02700

    [29] Zhang G J, Lu S J, Zhang W. CAD-net: a context-aware detection network for objects in remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 10015-10024(2019).

    [30] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C], 2999-3007(2017).

    [31] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 2117-2125(2017).

    [32] Xu C Y, Li C Z, Cui Z et al. Hierarchical semantic propagation for object detection in remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 4353-4364(2020).

    [33] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

    Luobing Wu, Yuhai Gu, Wenhao Wu, Shuaixin Fan. Remote Sensing Rotating Object Detection Based on Multi-Scale Feature Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228010
    Download Citation