Optics and Precision Engineering, Volume. 33, Issue 5, 789(2025)

End-to-end recognition of nighttime wildlife based on semi-supervised learning

Han LU1, Bolun CUI2, Huayang WAN1, Guofeng ZHANG1, Chen SHEN1, and Chi WANG1、*
Author Affiliations
  • 1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai200444, China
  • 2Beijing Institute of Space Mechanics & Electricity, Beijing100094, China
  • show less
    References(32)

    [1] DU N, FATHOLLAHI-FARD A M, WONG K Y. Wildlife resource conservation and utilization for achieving sustainable development in China: main barriers and problem identification[J]. Environmental Science and Pollution Research International, 1-20(2023).

    [2] 钟俊杰, 钮冰, 陈沁. 深度学习在野生动物保护中的应用[J]. 兽类学报, 43, 734-744(2023).

         ZHONG J J, NIU B, CHEN Q et al. Application of deep learning in wildlife conservation[J]. Acta Theriologica Sinica, 43, 734-744(2023).

    [3] VECVANAGS A, AKTAS K, PAVLOVS I et al. Ungulate detection and species classification from camera trap images using RetinaNet and faster R-CNN[J]. Entropy, 24, 353(2022).

    [4] 王驰, 沈晨, 黄庆. 夜间动物图像自监督学习增强与检测方法[J]. 中国光学(中英文), 17, 1087-1097(2024).

         WANG C, SHEN CH, HUANG Q et al. Self-supervised learning enhancement and detection methods for nocturnal animal images[J]. Chinese Optics, 17, 1087-1097(2024).

    [5] 仝召茂, 陈学海, 马志艳. 融合图像增强和迁移学习的YOLOv8n夜间苹果检测方法[J]. 华中农业大学学报, 43, 1-9(2024).

         TONG ZH M, CHEN X H, MA ZH Y et al. A method for detecting apple at night based on YOLOv8n with fusion of image enhancement and transfer learning[J]. Journal of Huazhong Agricultural University, 43, 1-9(2024).

    [6] 石志城, 卢汉, 沈晨. 夜间动物目标的被动式快速监测方法[J]. 中国测试: 1(8).

         SHI ZH CH, LU H, SHEN C et al. Passive rapid monitoring method for nocturnal animal targets[J]. China Measurement & Test: 1(8).

    [7] LIU Z Y, WANG B, LI Y et al. UnitModule: a lightweight joint image enhancement module for underwater object detection[J]. Pattern Recognition, 151, 110435(2024).

    [8] GUO Q R, WANG Y H, ZHANG Y J et al. AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement[J]. Science Progress, 107, 368504241263165(2024).

    [9] WANG T Y, REN S Y, ZHANG H Y. Nighttime wildlife object detection based on YOLOv8-night[J]. Electronics Letters, 60(2024).

    [10] RIVERA K, FIDINO M, LEHRER E W et al. Optimizing community science contributions in ecology: a case study on Zooniverse's 'Chicago wildlife watch'[J]. Biological Conservation, 292, 110490(2024).

    [12] TABAK M A, FALBEL D, HAMZEH T et al. CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence[J]. bioRxiv, 479461(2022).

    [13] BEERY S, MORRIS D, YANG S Y. Efficient pipeline for camera trap image review[J]. CoRR(2019).

    [14] CELIS G, UNGAR P, SOKOLOV A et al. A versatile, semi-automated image analysis workflow for time-lapse camera trap image classification[J]. Ecological Informatics, 81, 102578(2024).

    [15] TABAK M A, NOROUZZADEH M S, WOLFSON D W et al. Machine learning to classify animal species in camera trap images: Applications in ecology[J]. Methods in Ecology and Evolution, 10, 585-590(2019).

    [17] JEONG J, LEE S, KIM J, KWAK N. Consistency-based semi-supervised learning for object detection[C](2019).

    [18] LIU Y C, MA C Y, HE Z J et al. Unbiased teacher for semi-supervised object detection[J]. ArXiv e-Prints(2021).

    [19] XU M D, ZHANG Z, HU H et al. End-to-end semi-supervised object detection with soft teacher[C], 10, 2021(2021).

    [20] ZHANG J C, LIN X R, ZHANG W et al. Semi-DETR: semi-supervised object detection with detection transformers[C], 17, 2023(2023).

    [21] 杜运亮, 王明甲. 基于半监督域适应的微弱光环境下行人检测研究[J]. 电子测量与仪器学报, 38, 106-113(2024).

         DU Y L, WANG M J. Research on pedestrian detection in low-light conditions based on semi-supervised domain adaptation[J]. Journal of Electronic Measurement and Instrumentation, 38, 106-113(2024).

    [22] 方伟, 汤淼, 闫文君. 基于CenterNet的半监督起落架自动标注[J]. 兵器装备工程学报, 44, 239-244(2023).

         FANG W, TANG M, YAN W J et al. Automatic marking of semi-supervised landing gears based on CenterNet[J]. Journal of Ordnance Equipment Engineering, 44, 239-244(2023).

    [24] QIN X, WANG Z, BAI Y et al. FFA-Net: feature fusion attention network for single image dehazing[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11908-11915(2020).

    [25] VÉLEZ J, MCSHEA W, SHAMON H et al. An evaluation of platforms for processing camera-trap data using artificial intelligence[J]. Methods in Ecology and Evolution, 14, 459-477(2023).

    [26] BEERY S, MORRIS D, PERONA P. The iWildCam 2019 challenge dataset[J]. CoRR(2019).

    Tools

    Get Citation

    Copy Citation Text

    Han LU, Bolun CUI, Huayang WAN, Guofeng ZHANG, Chen SHEN, Chi WANG. End-to-end recognition of nighttime wildlife based on semi-supervised learning[J]. Optics and Precision Engineering, 2025, 33(5): 789

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Dec. 24, 2024

    Accepted: --

    Published Online: May. 20, 2025

    The Author Email:

    DOI:10.37188/OPE.20253305.0789

    Topics