[1] KONG T, YAO A B, CHEN Y R et al. HyperNet： towards accurate region proposal generation and joint object detection[C], 27, 845-853(2016).

[2] CHEN T, SAXENA S, LI L L et al. Pix2seq： a language modeling framework for object detection[webpage], 2109-10852. https：//arxiv.org/abs/2109.10852v2

[3] KHANAM R, HUSSAIN M. YOLOv11： an overview of the key architectural enhancements[webpage], 2410-17725. https：//arxiv.org/abs/2410.17725v1

[4] OU J P, ZHANG J. Investigation on recognition performance of harvesting robot using regions of interest histogram of oriented gradients feature based on improved fuzzy least square support vector machine[J]. Mathematical Problems in Engineering, 2021, 6650367(2021).

[5] CAI Y D, ZHANG D P, SUN Z M et al. YOLOv8 model-based additive manufacturing micro porosity defect detection and its dimension measurement[J]. Opt. Precision Eng., 32, 3222-3230(2024).

     蔡引娣, 张殿鹏, 孙梓盟. 基于改进YOLOv8模型的增材制造微小气孔缺陷检测及其尺寸测量[J]. 光学 精密工程, 32, 3222-3230(2024).

[6] JIANG ZH J, WU B J, MA L et al. A self correcting low-light object detection method based on pyramid edge enhancement[J]. Opt. Precision Eng., 32, 3099-3111(2024).

     蒋占军, 吴佰靖, 马龙. 基于金字塔边缘增强的自矫正低光照目标检测[J]. 光学 精密工程, 32, 3099-3111(2024).

[7] 宋宗莹, 王兴中, 曾杉. 基于改进YOLOv7的无人机图像铁路接触网部件目标检测方法[J]. 测绘通报, 108-114(2024).

     SONG Z Y, WANG X Z, ZENG S et al. Target detection method of railway catenary components in UAV images based on improved YOLOv7[J]. Bulletin of Surveying and Mapping, 108-114(2024).

[8] LAN G W, XU Z R, REN X Y et al. An improved algorithm for detecting components of power transmission lines from aerial inspection images[J]. Bulletin of Surveying and Mapping, 2024, 38-43， 49.

     蓝贵文, 徐梓睿, 任新月. 基于YOLOv8n改进的航拍输电线路图像多类电力部件检测算法[J]. 测绘通报, 38-43， 49(2024).

[9] ALQAHTANI H, KUMAR G. Deep learning-based intrusion detection system for in-vehicle networks with knowledge graph and statistical methods[J]. International Journal of Machine Learning and Cybernetics, 16, 3539-3555(2025).

[10] CHEN X L, LI L-J, LI F-F et al. Iterative visual reasoning beyond convolutions[C], 18, 7239-7248(2018).

[11] CHEN Z M, WEI X S, WANG P et al. Multi-label image recognition with graph convolutional networks[C], 15, 5172-5181(2019).

[12] HU H, GU J Y, ZHANG Z et al. Relation networks for object detection[C], 18, 3588-3597(2018).

[13] FANG Y, KUAN K, LIN J et al. Object detection meets knowledge graphs[C], 19, 1661-1667(2017).

[14] JIANG C, XU H, LIANG X et al. Hybrid knowledge routed modules for large-scale object detection[J]. Advances in Neural Information Processing Systems, 31(2018).

[15] XU H, JIANG C H, LIANG X D et al. Reasoning-RCNN： unifying adaptive global reasoning into large-scale object detection[C], 15, 6412-6421(2019).

[16] XU H, JIANG C H, LIANG X D et al. Spatial-aware graph relation network for large-scale object detection[C], 15, 9290-9299(2019).

[17] RIDNIK T, BEN-BARUCH E et al. ImageNet-21K pretraining for the masses[webpage], 2104-10972. https：//arxiv.org/abs/2104.10972v4

[18] PENNINGTON J, SOCHER R, MANNING C. Glove： global vectors for word representation[C], 1532-1543(2014).

[19] NAYEF B H, SULAIMAN R et al. Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks[J]. Multimedia Tools and Applications, 81, 2065-2094(2022).

[20] PRASETYO E, SUCIATI N, FATICHAH C. Yolov4-tiny with wing convolution layer for detecting fish body part[J]. Computers and Electronics in Agriculture, 198, 107023(2022).

[21] CHEN K, OUYANG W, LOY C C et al. Hybrid task cascade for instance segmentation[C], 15, 4969-4978(2019).