Lightweight Real-Time Target Detection Model for Remote Sensing Images

Yuhuan Li; Jie Wang; Li Lu; Ying Nie

doi:10.3788/LOP202158.1615007

[1] 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, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

[2] 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).

[3] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).

[4] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2020-10-19]. https://arxiv.org/abs/1804.02767

[5] Bochkovskiy A, Wang C Y, Liao H Y. YOLOv4:optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2020-10-19]. https://arxiv.org/abs/2004.10934

[6] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

[7] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2020-10-19]. https://arxiv.org/abs/1409.1556

[8] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

[9] Iandola F N, Han S, Moskewicz M W et al. SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and <0.5 MB model size[EB/OL]. (2016-11-04)[2020-10-19]. https://arxiv.org/abs/1602.07360

[10] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 15523970(2015).

[11] Lin M, Chen Q, Yan S. Network in network[C]. //International Conference on Learning Representations, April 14-16, 2014, Banff, AB, Canada. [S.l.: s.n.](2014).

[12] Wang C Y, Mark Liao H Y, Wu Y H et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 14-19, 2020, Seattle, WA, USA, 1571-1580(2020).

[13] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).

[14] Qin Z, Li Z M, Zhang Z N et al. ThunderNet: towards real-time generic object detection on mobile devices[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea, 6717-6726(2019).

[15] Huang Z T, Arifu K. Real-time detection of pedestrian and vehicle targets under uav platform[J/OL]. Computer Engineering and Application. (2020-08-06)[2020-10-19]. http://kns.cnki.net/kcms/detail/11.2127.TP.20200806.1353.018.html.

[16] Lü P F, Wang S G. Intelligent target recognition method of unmanned aerial vehicle combat platform[J]. Laser & Optoelectronics Progress, 56, 071001(2019).

[17] Szegedy C, Ioffe S, Vanhoucke V et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[EB/OL]. (2016-08-23)[2020-10-19]. https://arxiv.org/abs/1602.07261

[18] Li C Y, Yao J M, Lin Z X et al. Object detection method based on improved YOLO lightweight network[J]. Laser & Optoelectronics Progress, 57, 141003(2020).

[19] Huang T, Zhao S F, Bai Y R et al. Method of real-time road target depth neural network detection for UAV flight control platform[J]. Laser & Optoelectronics Progress, 57, 041509(2020).

[20] Sun J, Guo D B, Yang T T et al. Real-time object detection based on improved YOLOv3 network[J]. Laser & Optoelectronics Progress, 57, 221505(2020).

[21] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 936-944(2017).

[22] Xiao Z F, Liu Q, Tang G F et al. Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images[J]. International Journal of Remote Sensing, 36, 618-644(2015).