[1] LI Xiaoning, LEI Tao, ZHONG Jiandan et al. Detecting method of small vehicle targets based on improved SSD[J]. Journal of Applied Optics, 41, 150-155(2020).

[2] LIAO Yanna, YAO Liang. Bridge disease detection and recognition based on improved YOLOX algorithm[J]. Journal of Applied Optics, 44, 792-800(2023).

[3] ZAIDI S S A, ANSARI M S, ASLAM A et al. A survey of modern deep learning based object detection models[EB/OL]. http://arxiv.org/abs/2104.11892v1

[4] CHEN Y, LI W, SAKARIDIS C et al. Domain adaptive faster R-CNN for object detection in the wild[C], 3339-3348(2018).

[5] HE Z, ZHANG L. Multi-adversarial faster-rcnn for unrestricted object detection[C], 6668-6677(2019).

[6] KIM T, JEONG M, KIM S et al. Diversify and match: a domain adaptive representation learning paradigm for object detection[C], 12456-12465(2019).

[7] CHEN C, ZHENG Z, DING X et al. Harmonizing transferability and discriminability for adapting object detectors[C], 8869-8878(2020).

[8] VS V, GUPTA V, OZA P et al. Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection[C], 4516-4526(2021).

[9] XING W, ZHANG H, CHEN H et al. Feature adaptation-based multipeak-redetection spatial-aware correlation filter for object tracking[J]. Neurocomputing, 488, 299-314(2022).

[10] ZHANG H, LI Y, LIU H et al. Learning response-consistent and background-suppressed correlation filters for real-time UAV tracking[J]. Sensors, 23, 2980-3001.(2023).

[11] REN S, HE K, 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(2015).

[12] ZHANG H, XING W, YANG Y et al. SiamST: siamese network with spatio-temporal awareness for object tracking[J]. Information Sciences, 634, 122-139(2023).

[13] GANIN Y, LEMPITSKY V. Unsupervised domain adaptation by backpropagation[C], 1180-1189(2015).

[14] HSU C C, TSAI Y H, LIN Y Y et al. Every pixel matters: center-aware feature alignment for domain adaptive object detector[C], 733-748(2020).

[15] HE K, FAN H, WU Y et al. Momentum contrast for unsupervised visual representation learning[C], 9729-9738(2020).

[16] LOU Shuli, WANG Yan, GUO Jianqin et al. Infrared ship target detection algorithm based on improved YOLOX-S[J]. Journal of Applied Optics, 44, 1054-1060(2023).

[17] CORDTS M, OMRAN M, RAMOS S et al. The cityscapes dataset for semantic urban scene understanding[EB/OL]. http://download.visinf.tu-darmstadt.de/papers/2016-cvpr-cordts-cityscapes-preprint.pdf

[18] SAKARIDIS C, DAI D, GOOL L V. Semantic foggy scene understanding with synthetic data[EB/OL]. https://www.baidu.com/link?url=lX4kFbBqDNKz1JWpmt1YfKLcd0QtmaoMLIB3lEoRT2vaiaZjIYyd7CMRHwb9yjeM&wd=&eqid=c3bc4e3c00f50c840000000666cee097

[19] MATTOLIN G, ZANELLA L, RICCI E et al. ConfMix: unsupervised domain adaptation for object detection via confidence-based mixing[C], 423-433(2023).

[20] YU F X, WANG D, CHEN Y P et al. Style and content gaps aware unsupervised domain adaptation for object detection[C], 1061-1070(2022).

[21] ZHOU W Z, DU D W, ZHANG L B et al. Multi-granularity alignment domain adaptation for object detection[C], 9581-9590(2022).

[22] FARZANEH R, RAKSHITH S, RAHAF A et al. Seeking similarities over differences: similarity-based domain alignment for adaptive object detection[C], 9204-9213(2021).

[23] VAN D M L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 9, 2579-2605(2008).

[24] ZHANG Q, LI J, YAO M et al. Vargnet: variable group convolutional neural network for efficient embedded computing[EB/OL]. http://arxiv.org/pdf/1907.05653