JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (1): 76-84.doi: 10.6040/j.issn.1671-9352.5.2025.118
Previous Articles Next Articles
ZOU Zheng1, LEI Yusheng1, LIU Shijian2, WANG Dingyi3, QIU Xuewei1, SHI Wenwen2, ZHOU Xiaotong2
CLC Number:
| [1] 黄复生,朱世模,平正明,等. 中国动物志(昆虫纲)[M]. 北京:科学出版社,2000:1-961. HUANG Fusheng, ZHU Shimo, PING Zhengming, et al. Fauna sinica(insecta)[M]. Beijing: Science Press, 2000:1-961. [2] 白明,杨星科. 三维几何形态学概述及其在昆虫学中的应用[J]. 昆虫学报,2014,57(9):1105-1111. BAI Ming, YANG Xingke. A review of three-dimensional(3D)geo-metric morphometrics and its application in entomology[J]. Acta Entomologica Sinica, 2014, 57(9):1105-1111. [3] HOANG H N, HAI B H, PHUONG H L, et al. A lightweight keypoint matching framework for insect wing morphometric landmark detection[J]. Ecological Informatics, 2022, 70:101694. [4] JOÃO P R, WALTER G, ALICE M P. Automatic wing geometric morphometrics classification of honey bee(apis mellifera)subspecies using deep learning for detecting land-marks[J]. Big Data and Cognitive Computing, 2022, 6(3):70-70. [5] WANG Zejun, ZHANG Shihao, CHEN Lijiao, et al. Microscopic insect pest detection in tea plantations: improved YOLOv8 model based on deep learning[J]. Agriculture, 2024, 14(10):1739. [6] BERECIARTUA-PÉREZ A, GOMEZ L, PICON A, et al. Insect counting through deep learning-based density maps estimation[J]. Computers and Electronics in Agriculture, 2022, 197:106933. [7] BUSCHBACHER K, AHRENS D, ESPELAND M, et al. Image-based species identification of wild bees using convolutional neural networks[J]. Ecological Informatics, 2020, 55:101017. [8] BADGUJAR C M, ARMSTRONG P R, GERKEN A R, et al. Identifying common stored product insects using automated deep learning methods[J]. Journal of Stored Products Research, 2023, 103:102166. [9] VENEGAS P, CALDERON F, RIOFRÍO D, et al. Automatic ladybird beetle detection using deep-learning models[J]. PLoS One, 2021, 16(6):e0253027. [10] TETILA E C, MACHADO B B, MENEZES G V, et al. A deep-learning approach for automatic counting of soybean insect pests[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(10):1837-1841. [11] 唐灿,唐亮贵,刘波. 图像特征检测与匹配方法研究综述[J]. 南京信息工程大学学报(自然科学版),2020,12(3):261-273. TANG Can, TANG Lianggui, LIU Bo. A survey of image feature detection and matching methods[J]. Journal of Nanjing University of Information Science and Technology(Natural Science Edition), 2020, 12(3):261-273. [12] 江铁,朱桂斌,孙奥,等. 特征点提取算法性能分析研究[J]. 科学技术与工程,2012,12(30):7924-7930. JIANG Tie, ZHU Guibin, SUN Ao, et al. Performance analysis of feature point detectors[J]. Science Technology and Engineering, 2012, 12(30):7924-7930. [13] YANG Jieren, ZHANG xinying, LUO Peng. A summary of feature point detection based on image mosaics[J]. Electronic Test, 2021(6):53-54. [14] LE V, BEURTON-AIMAR M, ZEMMARI A, et al. Automated land-marking for insects morphometric analysis using deep neural networks[J]. Ecological Informatics, 2020, 60:101175. [15] GELDENHUYS D S, JOSIAS S, BRINK W, et al. Deep learning approaches to landmark detection in tsetse wing images[J]. PLOS Computational Biology, 2023, 19(6):e1011194. [16] 安胜彪,娄慧儒,陈书旺,等. 基于深度学习的旋转目标检测方法研究进展[J]. 电子测量技术,2021,44(21):168-17. AN Shengbiao, LOU Huiru, CHEN Shuwang, et al. Research progress of rotating target detection methods based on deep learning[J]. Electronic Measurement Technology, 2021, 44(21):168-178. [17] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9):1627-1645. [18] 陈天鹏,胡建文. 基于深度学习的遥感图像旋转目标检测研究综述[J]. 计算机应用研究,2024,41(2):329-340. CHEN Tianpeng, HU Jianwen. Overview of oriented object detection based on deep learning in remote sensing[J]. Application Research of Computers, 2024, 41(2):329-340. [19] LONG Wen, YU Cheng, YI Fang, et al. A comprehensive survey of oriented object detection in remote sensing images[J]. Expert Systems with Applications, 2023, 224:16-26. [20] 王旭,吴艳霞,张雪,等. 计算机视觉下的旋转目标检测研究综述[J]. 计算机科学,2023,50(8):79-92. WANG Xiu, WU Yanxia, ZHANG Xue, et al. Survey of rotating object detection research in computer vision[J]. Computer Science, 2023, 50(8):79-92. [21] YUAN Shuai, QIN Hanlin, YAN Xiang, et al. Sctransnet: spatial-channel cross transformer network for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62(10):1-15. [22] YANG Zhigang, XIA Xiangyu, LIU Yiming, et al. LPST-Det: local-perception-enhanced swin transformer for sar ship detection[J]. Remote Sensing, 2024, 16(3):18. [23] KONG Tao, SUN Fuchun, LIU Huaping, et al. Foveabox: beyond anchor-based object detection[J]. IEEE Transactions on Image Processing, 2020, 29:7389-7398. [24] QIAN Wen, YANG Xue, PENG Silong, et al. Learning modulated loss for rotated object detection[EB/OL].(2019-11-09)[2025-08-15]. https://doi.org/10.48550/arXiv.1911.08299. [25] HAN Jiaming, DING Jian, LI Jie, et al. Align deep features for oriented object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 10(99):1-11. [26] 中华人民共和国海关总署. SN/T 1105-2019大家白蚁检疫鉴定方法[S]. 北京:中华人民共和国海关总署,2019. General Administration of Customs of the Peoples Republic of China. SN/T 1105-2019 Detection and identification of coptotermes curvignathus Holmgren[S]. Beijing: General Administration of Customs of the Peoples Republic of China, 2019. [27] HOU Qibin, ZHOU Daquan, FENG Jiashi. Coordinate attention for efficient mobile network design[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE, 2021:13708-13717. [28] WANG Chenyao, LIAO Hongyuan, WU Yuehua, et al. CSPNET: a new backbone that can enhance learning capability of CNN[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE, 2020:390-404. |
| [1] | Xia LIANG,Jie GUO. A method of online teaching platform selection based on online reviews [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(9): 108-118. |
| [2] | Chao LI,Wei LIAO. Chinese disease text classification model driven by medical knowledge [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 122-130. |
| [3] | Jie JI,Chengjie SUN,Lili SHAN,Boyue SHANG,Lei LIN. A prompt learning approach for telecom network fraud case classification [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 113-121. |
| [4] | Qi LUO,Gang GOU. Multimodal conversation emotion recognition based on clustering and group normalization [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 105-112. |
| [5] | Fengxu ZHAO,Jian WANG,Yuan LIN,Hongfei LIN. Probability distribution optimization model for learning to rank [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 95-104. |
| [6] | Xingyu HUANG,Mingyu ZHAO,Ziyu LYU. Category-wise knowledge probers for representation learning of graph neural networks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 85-94. |
| [7] | Liang GUI,Yao XU,Shizhu HE,Yuanzhe ZHANG,Kang LIU,Jun ZHAO. Factual error detection in knowledge graphs based on dynamic neighbor selection [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 76-84. |
| [8] | Ning XIAN,Yixing FAN,Tao LIAN,Jiafeng GUO. Noise network alignment method integrating multiple features [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 64-75. |
| [9] | Chengjie SUN,Zongwei LI,Lili SHAN,Lei LIN. A document-level event extraction method based on core arguments [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 53-63. |
| [10] | Peiyu LIU,Bowen YAO,Zefeng GAO,Wayne Xin ZHAO. Matrix product operator based sequential recommendation model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 44-52, 104. |
| [11] | Wei SHAO,Gaoyu ZHU,Lei YU,Jiafeng GUO. Dimensionality reduction and retrieval algorithms for high dimensional data [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 27-43. |
| [12] | Jiyuan YANG,Muyang MA,Pengjie REN,Zhumin CHEN,Zhaochun REN,Xin XIN,Fei CAI,Jun MA. Research on self-supervised pre-training for recommender systems [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(7): 1-26. |
| [13] | Haisu CHEN,Jiachun LIAO,Sicheng YAO. Identification and statistical analysis methods of personal information disclosure in open government data [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(3): 95-106. |
| [14] | Xin WEN,Deyu LI. The ML-KNN method based on attribute weighting [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(3): 107-117. |
| [15] | Xueqiang ZENG,Yu SUN,Ye LIU,Zhongying WAN,Jiali ZUO,Mingwen WANG. Emoji embedded representation based on emotion distribution [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(3): 81-94. |