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[1]薛继元, 冯文林, 赵芬, 等.太阳能电池板的输出特性与实际应用研究 [J].红外与激光工程, 2015, 44(1): 176-181.
[2]秦一伟.光伏系统中电池板效率分析 [J].电子技术与软件工程, 2018(3): 100.DOI: 10.20109/j.cnki.etse.2018.03.073.
[3]蒋琳, 苏建徽, 施永, 等.基于红外热图像处理的光伏阵列热斑检测方法 [J].太阳能学报, 2020, 41(8): 180-184.DOI: 10.19912/j.0254-0096.2020.08.025.
[4]麻瑞.基于视觉感知的光伏板表面异物检测方法研究与应用 [D].西安建筑科技大学, 2024.DOI: 10.27393/d.cnki.gxazu.2024.000802.
[5]YANG C, SUN F, ZOU Y, et al. A survey of photovoltaic panel overlay and fault detection methods [J]. Energies, 2024, 17(4): 837.
[6]DHANRAJ J A, MOSTAFAEIPOUR A, VELMURUGAN K, et al. An effective evaluation on fault detection in solar panels [J]. Energies, 2021, 14(22): 7770.
[7]TRIKI-LAHIANI A, ABDELGHANI A B B, SLAMA-BELKHODJA I. Fault detection and monitoring systems for photovoltaic installations: A review [J]. Renewable and Sustainable Energy Reviews, 2018, 82: 2680-2692.
[8]HONG Y Y, PULA R A. Methods of photovoltaic fault detection and classification: A review [J]. Energy Reports, 2022, 8: 5898-5929.
[9]LU S, WANG B, WANG H, et al. A real-time object detection algorithm for video [J]. Computers & Electrical Engineering, 2019, 77: 398-408.
[10]ZHAO L, LI S. Object detection algorithm based on improved YOLOv3 [J]. Electronics, 2020, 9(3): 537.
[11]TANG C, FENG Y, YANG X, et al. The object detection based on deep learning [C]//2017 4th international conference on information science and control engineering (ICISCE). IEEE, 2017: 723-728.
[12]XIAO Y, TIAN Z, YU J, et al. A review of object detection based on deep learning [J]. Multimedia Tools and Applications, 2020, 79: 23729-23791.
[13]SU B, ZHOU Z, CHEN H. PVEL-AD: A large-scale open-world dataset for photovoltaic cell anomaly detection [J]. IEEE Transactions on Industrial Informatics, 2022, 19(1): 404-413.
[14]WANG C Y, YEH I H, MARK LIAO H Y. YOLOv9: Learning what you want to learn using programmable gradient information [C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2024: 1-21.
[15]YASEEN M. What is YOLOv9: An in-depth exploration of the internal features of the next-generation object detector [J]. arXiv preprint arXiv: 2409.07813, 2024.
[16]GUI H, SU T, JIANG X, et al. FS-YOLOv9: a frequency and spatial feature-based YOLOv9 for real-time breast cancer detection [J]. Academic Radiology, 2025, 32(3): 1228-1240.
[17]CHIEN C T, JU R Y, CHOU K Y, et al. YOLOv9 for fracture detection in pediatric wrist trauma X‐ray images [J]. Electronics Letters, 2024, 60(11): e13248.
[18]LU D, WANG Y. MAR-YOLOv9: A multi-dataset object detection method for agricultural fields based on YOLOv9 [J]. Plos one, 2024, 19(10): e0307643.
[19]LI J, FENG Y, SHAO Y, et al. IDP-YOLOV9: Improvement of Object Detection Model in Severe Weather Scenarios from Drone Perspective [J]. Applied Sciences, 2024, 14(12): 5277.
[20]YANG S, CAO Z, LIU N, et al. Maritime electro-optical image object matching based on improved YOLOv9 [J]. Electronics, 2024, 13(14): 2774.
[21]GU Z, ZHU K, YOU S. YOLO-ssfs: A method combining spd-conv/stdl/im-fpn/siou for outdoor small target vehicle detection [J]. Electronics, 2023, 12(18): 3744.
[22]SUN R, FAN H, TANG Y, et al. Research on small target detection algorithm for UAV inspection scene based on SPD-conv [C]//Proc. of SPIE Vol. 2024, 13063: 130632T-1.
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