[1] [1] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2014: 580-587.

[2] [2] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2016: 779-788.

[3] [3] LI Z, ZHOU F. FSSD: feature fusion single shot multibox detector[J/OL]. arXiv preprint arXiv, 2017, https://arxiv.org/abs/1712.00960.

[4] [4] Redmon J, Farhadi A. Yolov3: An incremental improvement[J/OL]. arXiv preprint arXiv, 2018, https://arxiv.org/abs/1804.02767.

[5] [5] Jocher G, Chaurasia A, Stoken A, et al. ultralytics/yolov5: v6.1 -TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference[Z/OL]. 2022, https://doi.org/10.5281/ZENODO.6222936.

[6] [6] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J/OL]. arXiv preprint arXiv, 2020, https://arxiv.org/abs/2004.10934#:~:text=%EE%80%80YOLOv4% 3A% 20Optimal%20Speed%20and%20Accuracy%20of%20Object%20Detect ion%EE%80%81.,features%20operate%20on%20certain%20models%2 0exclusively%20and%20.

[7] [7] 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[J]. arXiv preprint arXiv, 2022, https://arxiv.org/abs/2207.02696.

[8] [8] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2018: 8759-8768.

[9] [9] Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]// Conference on Computer Vision & Pattern Recognition. IEEE, 2017: 6517-6525.

[10] [10] 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 & Machine Intelligence, 2017, 39(6): 1137-1149.

[11] [11] He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961-2969.

[12] [12] ZHENG Z, WANG P, REN D, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 2021, 52(8): 8574-8586.

[13] [13] Veit A, Matera T, Neumann L, et al. Coco-text: Dataset and benchmark for text detection and recognition in natural images[J]. arXiv preprint arXiv, 2016, https://arxiv.org/abs/1601.07140.

[14] [14] Smith A R. Color gamut transform pairs[J]. ACM Siggraph Computer Graphics, 1978, 12(3): 12-19.

[15] [15] Zhou Z, Cao J, Wang H, et al. Image denoising algorithm via doubly bilateral filtering[C]// International Conference on Information Engineering and Computer Science. IEEE, 2009: 1-4.

[16] [16] Hoiem D, Divvala S K, Hays J H. Pascal VOC 2008 challenge[J]. Computer Science, 2009 https://www. semanticscholar.org/paper/Pascal-VOC-2008-Challenge-Hoiem-Divvala/9c327cf1bb8435a8fba27b6ace50bb907 078d8d1.

[17] [17] ZHAO W Y. Discriminant component analysis for face recognition[C]//Proceedings 15th International Conference on Pattern Recognition, IEEE, 2000, 2: 818-821.

[18] [18] Venkataraman V, FAN G, FAN X. Target tracking with online feature selection in FLIR imagery[C]// IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007: 1-8.

[19] [19] CHEN R, LIU S, MU J, et al. Borrow from source models: efficient infrared object detection with limited examples[J]. Applied Sciences, 2022, 12(4): 1896.

[20] [20] Kera S B, Tadepalli A, Ranjani J J. A paced multi-stage block-wise approach for object detection in thermal images[J]. The Visual Computer, 2022, https://doi.org/10.1007/s00371-022-02445-x.

[21] [21] Vadidar M, Kariminezhad A, Mayr C, et al. Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection[C]// IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022: 367-374.