[4] [4] DONG X, HUANNG X, ZHENG YY, et al. A novel innfrared small movving target detection method based on tracking innterest points unnder complicated backkground[J]. Infrareed Phys. Technol.,, 2014, 65: 36-42.

[5] [5] HE Y, LI M, ZHHANG J, et al. Smmall infrared targeet detection basedd on low-rank and spaarse representationn[J]. Infrared Physs. Technol., 2015, 68: 98-109.

[6] [6] GUO J, Hsia C, LIU Y, et al. Faast background suubtraction based oon a multilayer codeebook model forr moving objectt detection[C]//IEEEE Transactions on Circuits and Systeems for Video Techhnology, 2013, 23((10): 1809-1821.

[8] [8] XIA K J, CHENNG J, TAO D, et al. Liver detectio n algorithm basedd on an improved deeep network combbined with edge perception[J]. IEEEE ACCESS, 2019, 112: 175135-17514.

[9] [9] Redmon J, Farh adi A. YOLO90000: Better, Fasterr, Stronger[C]//IEEEE Conference on Computer Visioon and Pattern Recognition, 20017: 6517-6525.

[10] [10] YU Z, FENG C, LIU M Y, ett al. CASENet: DDeep category-awware semantic edge ddetection[C]// Commputer Vision & Pattern RecognitioPnn of IEEE, 2017: 17661-1770(doi: 10.11109/CVPR.2017.191).

[11] [11] Neubeck A, Gool L V. Efficient non-maximum suppression[C]//International Conference on Pattern Recognition, IEEE Computer Society, 2006: 850-855.

[15] [15] CHEN S, CHEN Z, XU X, et al. Nv-Net: efficient infrared image segmentation with convolutional neural networks in the low illumination environment[J]. Infrared Physics & Technology, 2020, 105: 103184.

[16] [16] HOSANG J, BENENSON R, SCHIELE B. A convnet for non-maximum suppression[C]//German Conference on Pattern Recognition, Cham: Springer, 2016: 192-204.

[17] [17] REDMON J, FARHADI A. YOLO9000: Better, faster, stronger[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York: IEEE Press, 2017: 7263-7271.

[18] [18] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.

[19] [19] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.

[20] [20] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.