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

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

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

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

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

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

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

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

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

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

[11] S REN, K HE, R GIRSHICK et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2015).

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

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

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

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

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

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

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

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

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

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

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

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

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