白蚁分区式微方向感知的精确形态识别

doi:10.6040/j.issn.1671-9352.5.2025.118

摘要/Abstract

摘要： 提出分区式微方向感知的自动形态分析方法,采用分区式思想细化目标,间接增加形态差异的显著性。选择旋转矩形表征目标和更多方向信息,在识别时引入多层局部空间感知模块,将方向与特征直接关联,同时融合双支空间金字塔模块,强化浅层特征复用,提高了计算效率。将主流旋转目标检测方法、关键点检测方法与本文方法对比,结果证明在提取高干扰的小目标的方向和位置方面,本文方法具有更好的精确度和鲁棒性。

关键词: 白蚁形态测量, 旋转目标检测, 关键点检测, 物种鉴定, 自动形态识别

Abstract: The partition-based approach is used in the paper to refine the target by indirectly enhancing the salience of morphological differences. Rotational moment-based representation is provided more directional information, and a multi-layer local spatial perception module is incorporated to directly associate direction with features. Furthermore, a dual-branch spatial pyramid module is introduced to enhance the reuse of shallow features and improve computational efficiency. In our experiments, the rotational object detection method, the key point detection method, and the proposed method are compared, and it is demonstrated that our method achieves better accuracy and robustness in extracting the direction and position of small targets under higher interference.

Key words: measurement of termite morphology, oriented object detection, key point detection, species identification, automatic morphology recognition

中图分类号:

TP391

邹峥,雷雨晟,刘石坚,王定一,邱学炜,史雯雯,周校通. 白蚁分区式微方向感知的精确形态识别[J]. 《山东大学学报(理学版)》, 2026, 61(1): 76-84.

ZOU Zheng, LEI Yusheng, LIU Shijian, WANG Dingyi, QIU Xuewei, SHI Wenwen, ZHOU Xiaotong. Precise morphological recognition with zonal micro-direction for termites[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2026, 61(1): 76-84.

参考文献

[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 Peoples Republic of China. SN/T 1105-2019 Detection and identification of coptotermes curvignathus Holmgren[S]. Beijing: General Administration of Customs of the Peoples 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.

相关文章 15

[1]	梁霞,郭洁. 基于在线评论的线上教学平台选择方法[J]. 《山东大学学报(理学版)》, 2024, 59(9): 108-118.
[2]	黎超,廖薇. 基于医疗知识驱动的中文疾病文本分类模型[J]. 《山东大学学报(理学版)》, 2024, 59(7): 122-130.
[3]	纪杰,孙承杰,单丽莉,尚伯乐,林磊. 基于提示学习的电信网络诈骗案件分类方法[J]. 《山东大学学报(理学版)》, 2024, 59(7): 113-121.
[4]	罗奇,苟刚. 基于聚类和群组归一化的多模态对话情绪识别[J]. 《山东大学学报(理学版)》, 2024, 59(7): 105-112.
[5]	赵峰叙,王健,林原,林鸿飞. 面向排序学习的概率分布优化模型[J]. 《山东大学学报(理学版)》, 2024, 59(7): 95-104.
[6]	黄兴宇,赵明宇,吕子钰. 面向图神经网络表征学习的类别知识探针[J]. 《山东大学学报(理学版)》, 2024, 59(7): 85-94.
[7]	桂梁,徐遥,何世柱,张元哲,刘康,赵军. 基于动态邻居选择的知识图谱事实错误检测方法[J]. 《山东大学学报(理学版)》, 2024, 59(7): 76-84.
[8]	咸宁,范意兴,廉涛,郭嘉丰. 融合多重特征的噪声网络对齐方法[J]. 《山东大学学报(理学版)》, 2024, 59(7): 64-75.
[9]	孙承杰,李宗蔚,单丽莉,林磊. 一种基于核心论元的篇章级事件抽取方法[J]. 《山东大学学报(理学版)》, 2024, 59(7): 53-63.
[10]	刘沛羽,姚博文,高泽峰,赵鑫. 基于矩阵乘积算符表示的序列化推荐模型[J]. 《山东大学学报(理学版)》, 2024, 59(7): 44-52, 104.
[11]	邵伟,朱高宇,于雷,郭嘉丰. 高维数据的降维与检索算法[J]. 《山东大学学报(理学版)》, 2024, 59(7): 27-43.
[12]	杨纪元,马沐阳,任鹏杰,陈竹敏,任昭春,辛鑫,蔡飞,马军. 基于自监督的预训练在推荐系统中的研究[J]. 《山东大学学报(理学版)》, 2024, 59(7): 1-26.
[13]	陈海粟,廖佳纯,姚思诚. 政府开放数据中个人信息披露识别与统计方法[J]. 《山东大学学报(理学版)》, 2024, 59(3): 95-106.
[14]	温欣,李德玉. 基于属性加权的ML-KNN方法[J]. 《山东大学学报(理学版)》, 2024, 59(3): 107-117.
[15]	曾雪强,孙雨,刘烨,万中英,左家莉,王明文. 基于情感分布的emoji嵌入式表示[J]. 《山东大学学报(理学版)》, 2024, 59(3): 81-94.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed