References
- Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens. 65(1):2–16. doi:10.1016/j.isprsjprs.2009.06.004.
- Chen C, He C, Hu C, Pei H, Jiao L. 2019. A deep neural network based on an attention mechanism for sar ship detection in multiscale and complex scenarios. IEEE Access. 7:104848–104863. doi:10.1109/ACCESS.2019.2930939.
- Chen K, Lin W, Li J, See J, Wang J, Zou J. 2021. AP-loss for accurate one-stage object detection. IEEE Trans Pattern Anal Mach Intell. 43(11):3782–3798. doi:10.1109/tpami.2020.2991457.
- Cheng G, Han J, Lu X. 2017. Remote sensing image scene classification: benchmark and state of the art. Proc IEEE. 105(10):1865–1883. doi:10.1109/JPROC.2017.2675998.
- Cheng G, Han J. 2016. A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens. 117:11–28. doi:10.1016/j.isprsjprs.2016.03.014.
- Chini M, Pacifici F, Emery WJ, Pierdicca N, Del Frate F. 2008. Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats. IEEE Trans Geosci Remote Sensing. 46(6):1812–1821. doi:10.1109/TGRS.2008.916223.
- Chua LO. 1999. CNN: A paradigm for complexity[M]. In: Huertas JH, Chen WK, Madan RN, editors. Visions of nonlinear science in the 21st century: Festschrift dedicated to Leon O Chua on the occasion of his 60th birthday. Singapore: World Scientific Publishing; p. 529–837.
- Cui W, Wang F, He X, Zhang D, Xu X, Yao M, Wang Z, Huang J. 2019. Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model. Remote Sensing. 11(9):1044. doi:10.3390/rs11091044.
- Deng Z, Sun H, Zhou S, Zhao J, Lei L, Zou H. 2018. Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens. 145:3–22. doi:10.1016/j.isprsjprs.2018.04.003.
- Deren L, Liangpei Z, Guisong X. 2014. Automatic analysis and mining of remote sensing big data. Acta Geodetica et Cartographica Sinica. 43(12):1211. doi:10.13485/j.cnki.11-2089.2014.0187.
- Du L, Zhang R, Wang X. 2020. Overview of two-stage object detection algorithms. Paper presented at the Journal of Physics: Conference Series. doi:10.1088/1742-6596/1544/1/012033.
- Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q. 2019. Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. IEEE. doi:10.1109/iccv.2019.00667.
- Gao J, Zhao Y. 2021. TFE: a Transformer Architecture for Occlusion Aware Facial Expression Recognition. Front Neurorobot. 15:763100. doi:10.3389/fnbot.2021.763100.
- Girshick R. 2015. Fast r-cnn. Paper Presented at the Proceedings of the IEEE International Conference on Computer Vision.
- He K, Zhang X, Ren S, Sun J. 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 37(9):1904–1916. doi:10.1109/TPAMI.2015.2389824.
- Hinton GE, Salakhutdinov RR. 2006. Reducing the dimensionality of data with neural networks. Science. 313(5786):504–507. doi:10.1126/science.1127647.
- Jiang X, Gao T, Zhu Z, Zhao Y. 2021. Real-time face mask detection method based on YOLOv3. Electronics. 10(7):837. doi:10.3390/electronics10070837.
- Kotei E, Thirunavukarasu R. 2022. Ensemble technique coupled with deep transfer learning framework for automatic detection of tuberculosis from chest X-ray radiographs. Healthcare (Basel). 10(11):2335. doi:10.3390/healthcare10112335.
- LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521(7553):436–444. doi:10.1038/nature14539.
- Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W. 2022. YOLOv6: a single-stage object detection framework for industrial applications. doi:10.48550/arXiv.2209.02976.
- Li K, Wan G, Cheng G, Meng L, Han J. 2020. Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS J Photogramm Remote Sens. 159:296–307. doi:10.1016/j.isprsjprs.2019.11.023.
- Lorencin I, Anđelić N, Mrzljak V, Car Z, University of Rijeka Faculty of Engineering. 2019. Marine Objects Recognition Using Convolutional Neural Networks. Naše More. 66(3):112–120. doi:10.17818/NM/2019/3.3.
- Ma N, Zhang X, Zheng H-T, Sun J. 2018. Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV). Springer International Publishing. doi:10.1007/978-3-030-01264-9_8.
- Mufford JT, Hill DJ, Flood NJ, Church JS. 2019. Use of unmanned aerial vehicles (UAVs) and photogrammetric image analysis to quantify spatial proximity in beef cattle. J Unmanned Veh Sys. 7(3):194–206. doi:10.1139/juvs-2018-0025.
- Redmon J, Farhadi A. 2018. Yolov3: an incremental improvement. doi:10.48550/arXiv.1804.02767.
- Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S. 2019. Generalized intersection over union: a metric and a loss for bounding box regression. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 658–666.
- Santosh KC, Dhar MK, Rajbhandari R, Neupane A. 2020. Deep Neural Network for Foreign Object Detection in Chest X-Rays. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS),p. 538–541. IEEE. doi:10.1109/cbms49503.2020.00107.
- Tsai E-J, Yeh W-C. 2021. PAM: pose attention module for pose-invariant face recognition. doi:10.48550/arXiv.2111.11940.
- Wang C-Y, Bochkovskiy A, Liao H-YM. 2022. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. doi:10.48550/arXiv.2207.02696.
- Wang C-Y, Liao H-YM, Yeh I-H. 2022. Designing network design strategies through gradient path analysis. doi:10.48550/arXiv.2211.04800.
- Wang S, Wu H, Zhang L. 2021. AFDN: attention-based feedback dehazing network for UAV remote sensing image haze removal. In: 2021 IEEE International Conference on Image Processing (ICIP). IEEE. doi:10.1109/ICIP42928.2021.9506604.
- Xiao Y, Tian Z, Yu J, Zhang Y, Liu S, Du S, Lan X. 2020. A review of object detection based on deep learning. Multimed Tools Appl. 79(33-34):23729–23791. doi:10.1007/s11042-020-08976-6.
- Xie C, Zhu H, Fei Y. 2022. Deep coordinate attention network for single image super‐resolution. IET Image Proc. 16(1):273–284. doi:10.1049/ipr2.12364.
- Xu S, Guo Z, Liu Y, Fan J, Liu X. 2022. An improved lightweight yolov5 model based on attention mechanism for face mask detection. In: International Conference on Artificial Neural Networks. doi:10.1007/978-3-031-15934-3_44.
- Yang T-Y, Chen Y-T, Lin Y-Y, Chuang Y-Y. 2019. FSA-Net: learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1087–1096. doi:10.1109/CVPR.2019.00118.
- Yu H-J, Son C-H. 2020. Leaf spot attention network for apple leaf disease identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. IEEE. doi:10.1109/cvprw50498.2020.00034.
- Zhang L, Zhang L, Du B. 2016. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci Remote Sens Mag. 4(2):22–40. doi:10.1109/MGRS.2016.2540798.
- Zhang X, Zhou X, Lin M, Sun J. 2018. Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. doi:10.1109/cvpr.2018.00716.
- Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D. 2020. Distance-IoU loss: faster and better learning for bounding box regress ion. AAAI 34 (7):12993–13000. doi: 10.1609/aaai.v34i07.6999.
- Zhu X, Su W, Lu L, Li B, Wang X, Dai J. 2020. Deformable detr: deformable transformers for end-to-end object detection. doi:10.48550/arXiv.2010.04159.
- Zou Z, Chen K, Shi Z, Guo Y, Ye J. 2023. Object detection in 20 years: a survey. Proc IEEE. 111(3):257–276. doi:10.1109/JPROC.2023.3238524.