[1] Chen C L P, Li H, Wei Y T et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 574-581(2014).

[2] Gu Y F, Wang C, Liu B X et al. A kernel-based nonparametric regression method for clutter removal in infrared small-target detection applications[J]. IEEE Geoscience and Remote Sensing Letters, 7, 469-473(2010).

[3] Gao C Q, Meng D Y, Yang Y et al. Infrared patch-image model for small target detection in a single image[J]. IEEE Transactions on Image Processing, 22, 4996-5009(2013).

[4] Wang X Y, Peng Z M, Kong D H et al. Infrared dim target detection based on total variation regularization and principal component pursuit[J]. Image and Vision Computing, 63, 1-9(2017).

[5] Wang X, Lü G F, Xu L Z. Infrared dim target detection based on visual attention[J]. Infrared Physics & Technology, 55, 513-521(2012).

[6] Li X F, Wang S Q, Weng X et al. Remote sensing of floating macroalgae blooms in the East China Sea based on UNet deep learning model[J]. Acta Optica Sinica, 41, 0201002(2021).

[7] Dong X B, Huang X S, Zheng Y B et al. Infrared dim and small target detecting and tracking method inspired by human visual system[J]. Infrared Physics & Technology, 62, 100-109(2014).

[8] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[9] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2019).

[10] Dai Y M, Wu Y Q, Zhou F et al. Asymmetric contextual modulation for infrared small target detection[C], 949-958(2021).

[11] Lin L K, Wang S Y, Tang Z X. Point target detection in infrared over-sampling scanning images using deep convolutional neural networks[J]. Journal of Infrared and Millimeter Waves, 37, 219-226(2018).

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

[13] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C], 1520-1528(2015).

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

[15] Liu Z, Li J G, Shen Z Q et al. Learning efficient convolutional networks through network slimming[C], 2755-2763(2017).

[16] Huang Y F, Tang Z R, Chen D et al. Batching soft IoU for training semantic segmentation networks[J]. IEEE Signal Processing Letters, 27, 66-70(2020).

[17] Wang B, Ma F Q, Ge L J et al. Icing-EdgeNet: a pruning lightweight edge intelligent method of discriminative driving channel for ice thickness of transmission lines[J]. IEEE Transactions on Instrumentation and Measurement, 70, 20151878(2021).

[18] Jiang T X, Huang T Z, Zhao X L et al. Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm[J]. Journal of Computational and Applied Mathematics, 372, 112680(2020).

[19] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

[20] Wei X C, Liang Y, Li X H et al. TGPA: tile-grained pipeline architecture for low latency CNN inference[C], 18356926(2018).

[21] Zhang S F, Chi C, Yao Y Q et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C], 9756-9765(2020).

[22] Li J H, Zhang P, Wang X W et al. Infrared small-target detection algorithms: a survey[J]. Journal of Image and Graphics, 25, 1739-1753(2020).

[23] Wei J H, Lin J. Deep learning algorithms, hardware and their military applications[J]. Electronics & Packaging, 19, 1-6, 22(2019).

[24] Mei Y J, Wang W J, Peng X Z. Implementation of FPGA-based U-net network hardware acceleration system[J]. Electronics & Packaging, 20, 40-45(2020).