[1] [1] 郭峰， 朱启兵， 黄敏， 等. 基于改进YOLOV4的陶瓷基板瑕疵检测［J］. 光学 精密工程， 2022， 30（13）： 1631-1641. doi: 10.37188/OPE.20223013.1631GUOF， ZHUQ B， HUANGM， et al. Defect detection in ceramic substrate based on improved YOLOV4［J］. Opt. Precision Eng.， 2022， 30（13）： 1631-1641.（in Chinese）. doi: 10.37188/OPE.20223013.1631

[2] LI J C, FANG F M, MEI K F et al[M]. Multi-Scale Residual Network for Image Super-Resolution, 527-542(2018).

[3] QI L, KUEN J, GU J X et al. Multi-scale aligned distillation for low-resolution detection[C], 14438-14448(20).

[4] HINTON G, VINYALS O, DEAN J. Distilling The Knowledge in a Neural Network[webpage]. arXiv, 1503-02531(2015). https：//arxiv.org/abs/1503.02531.pdf

[5] HE T, SHEN C H, TIAN Z et al. Knowledge adaptation for efficient semantic segmentation[C], 578-587(15).

[6] LIU Y F, SHU C Y, WANG J D et al. Structured knowledge distillation for dense prediction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 7035-7049(2023).

[7] CHEN G B, CHOI W, YU X et al. Learning efficient object detection models with knowledge distillation[C], 742-751(2017).

[8] WANG T, YUAN L, ZHANG X P et al. Distilling object detectors with fine-grained feature imitation[C], 4928-4937(15).

[9] ZHANG L, MA K. Improve object detection with feature-based knowledge distillation： towards accurate and efficient detectors[C](2020).

[10] [10] 褚晶辉， 史李栋， 井佩光， 等. 适用于目标检测的上下文感知知识蒸馏网络［J］. 浙江大学学报（工学版）， 2022， 56（3）： 503-509.CHUJ H， SHIL D， JINGP G， et al. Context-aware knowledge distillation network for object detection［J］. Journal of Zhejiang University （Engineering Science）， 2022， 56（3）： 503-509.（in Chinese）

[11] HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C], 13708-13717(20).

[12] LIU S, QI L, QIN H F et al. Path aggregation network for instance segmentation[C], 8759-8768(18).

[13] [13] 黄震华， 杨顺志， 林威， 等. 知识蒸馏研究综述［J］. 计算机学报， 2022， 45（3）624-653. doi: 10.11897/SP.J.1016.2022.00624HUANGZ H， YANGS Z， LINW， et al. Knowledge distillation： a survey［J］. Chinese Journal of Computers， 2022， 45（3）624-653. （in Chinese）. doi: 10.11897/SP.J.1016.2022.00624

[14] FU K, SHI P P, SONG Y F et al. Ultrafast video attention prediction with coupled knowledge distillation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 10802-10809(2020).

[15] LI B Y, LIU Y, WANG X G. Gradient harmonized single-stage detector[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 8577-8584(2019).

[16] KISANTAL M, WOJNA Z, MURAWSKI J et al. Augmentation for Small Object Detection[webpage]. arXiv, 1902-07296(2019). https：//arxiv.org/abs/1902.07296.pdf

[17] 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).

[18] CAI Z W, VASCONCELOS N. Cascade R-CNN： delving into high quality object detection[C], 6154-6162(18).

[19] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4： Optimal Speed and Accuracy of Object Detection[webpage]. arXiv, 2004-10934(2020). https：//arxiv.org/abs/2004.10934.pdf

[20] GE Z, LIU S, WANG F et al. YOLOX： Exceeding YOLO Series in 2021[webpage]. arXiv, 2107-08430(2021). https：//arxiv.org/abs/2107.08430.pdf

[21] LI C, LI L, JIANG H et al. YOLOv6： A Single-Stage Object Detection Framework for Industrial Applications[webpage]. arXiv, 2209-02976(2022). https：//arxiv.org/abs/2209.02976.pdf

[22] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7： Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors[webpage]. arXiv, 2207-02696(2022). https：//arxiv.org/abs/2207.02696.pdf