Lightweight Remote Sensing Object Detector based on YOLOX-Tiny

Lei Lang; Kuan Liu; Dong Wang

doi:10.3788/LOP212699

[1] Li S T, Li C Y, Kang X D. Development status and future prospects of multi-source remote sensing image fusion[J]. National Remote Sensing Bulletin, 25, 148-166(2021).

[2] Zhou C H, Sun J L, Su F Z et al. Geographic information science development and technological application[J]. Acta Geographica Sinica, 75, 2593-2609(2020).

[3] Nie G T, Huang H. A survey of object detection in optical remote sensing images[J]. Acta Automatica Sinica, 47, 1749-1768(2021).

[4] Zhao Z Q, Zheng P, Xu S T et al. Object detection with deep learning: a review[J]. IEEE Transactions on Neural Networks and Learning Systems, 30, 3212-3232(2019).

[5] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

[6] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[7] 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).

[8] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 386-397(2020).

[9] Zhao X L, Zhang J, Tian J M et al. Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention[J]. International Journal of Remote Sensing, 42, 5764-5783(2021).

[10] Dai Y, Yi B S, Xiao J S et al. Object detection of remote sensing image based on improved rotation region proposal network[J]. Acta Optica Sinica, 40, 0111020(2020).

[11] Wu X N, He P, Deng Z G et al. A deep learning model of small object detection based on attention mechanism[J]. Computer Engineering & Science, 43, 95-104(2021).

[12] Wang C, Zhang H H, Yue J W et al. Object detection to the pine trees affected by pine wilt disease in remote sensing images using deep learning[J]. Journal of Nanjing Normal University (Natural Science Edition), 44, 84-89(2021).

[13] Li Z Q, Zhu R F, Ma J Y et al. Airport detection method combined with continuous learning of residual-based network on remote sensing image[J]. Acta Optica Sinica, 40, 1628005(2020).

[14] Nong Y J, Wang J J. Real-time object detection in remote sensing images based on embedded system[J]. Acta Optica Sinica, 41, 1028001(2021).

[15] Shen F Y, Wang Q, Jiang J F. Real-time automatic ship detection based on modified convolutional neural networks[J]. Application Research of Computers, 37, 316-319(2020).

[16] Yan H W, Zhang J, Yan Z L et al. Tower recognition of satellite imagery with improved CenterNet model[J]. Remote Sensing Information, 36, 84-91(2021).

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

[18] Wang C Y, Bochkovskiy A, Liao H Y M. Scaled-YOLOv4: scaling cross stage partial network[C], 13024-13033(2021).

[19] Ge Z, Liu S T, Wang F et al. YOLOX: exceeding YOLO series in 2021[EB/OL]. https://arxiv.org/abs/2107.08430

[20] Wang C Y, Liao H Y M, Wu Y H et al. CSPNet: a new backbone that can enhance learning capability of CNN[C], 1571-1580(2020).

[21] Zhang H Q, Ban Y M, Guo L L et al. Detection method of remote sensing image ship based on YOLOv5[J]. Electronic Measurement Technology, 44, 87-92(2021).

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

[23] Li H C, Xiong P F, An J et al. Pyramid attention network for semantic segmentation[EB/OL]. https://arxiv.org/abs/1805.10180

[24] Yu P D, Wang X, Jiang G W et al. A typical target detection algorithm in remote sensing images based on improved YOLOv4[J]. Journal of Geomatics Science and Technology, 38, 280-286(2021).

[25] Li H G, Yu R N, Ding W R. Research development of small object traching based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 42, 024691(2021).

[26] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

[27] Zhu X Z, Hu H, Lin S et al. Deformable ConvNets V2: more deformable, better results[C], 9300-9308(2019).

[28] Zheng Z H, Wang P, Liu W et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12993-13000(2020).

[29] Zhou W. L1-norm minimization algorithms and applications[D](2013).

[30] 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, 159, 296-307(2020).

[31] Chen K, Wang J Q, Pang J M et al. MMDetection: open MMLab detection toolbox and benchmark[EB/OL]. https://arxiv.org/abs/1906.07155v1

[32] Gao X B, Mo M J C, Wang H T et al. Recent advances in small object detection[J]. Journal of Data Acquisition and Processing, 36, 391-417(2021).

[33] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8693, 740-755(2014).

[34] Huang W, Li G Y, Chen Q Q et al. CF2PN: a cross-scale feature fusion pyramid network based remote sensing target detection[J]. Remote Sensing, 13, 847(2021).

[35] Xu T, Sun X, Diao W H et al. ASSD: feature aligned single-shot detection for multiscale objects in aerial imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5607117(2022).

[36] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[37] Huang Z C, Li W, Xia X G et al. LO-det: lightweight oriented object detection in remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5603515(2022).

[38] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).