半监督式野生动物夜间目标端到端检测

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

LU Han1，CUI Bolun²，WAN Huayang1， ZHANG Gueng1，SHEN Chen1，WANG Chil*

（1. School ， ， ，

2. & Electricity， lOoo94，China） *Correspondingauthor，E-mail：wangchi@shu.edu.cn

Abstract： This study addresses the challenges low accuracy efficiency in the detection wildlife at night，as well as the dificulties associated with manual comprehensive labeling.An end-to-end recognition model for nightime wildlife based on semi-supervised learning（SAN-YOLO）was proposed investigated.A feature atention mechanism a pixel attention mechanism were integrated within the YOLOv8 framework to enhance the adaptability feature representation capabilities the detector for nocturnal mages. Subsequently，a semi-supervised training network based on a teacher-student learning paradigm was constructed，allowing the student model to learn from a substantial number unlabeled original images by generating appropriately asigning pseudo-labels. The efficacy the constructed dataset was then evaluated. Experimental results demonstrate that the mean Average Precision （mAP） SAN

YOLO reaches 69.7% with only 5% annotated data，surpassing the 59.6% mAP achieved with full su pervision in its conventional detector exceeding the baseline model's performance 57.1% . Consequently，the proposed detection method exhibits robust performance with a limited number labeled datasets for nocturnal animals validates the effectiveness attention mechanisms in the domain nighttime object detection.

Key words： object detection；semi-supervised learning；infrared nightvision；wildlife conservation; teacher-student model； attention mechanism

1引言

野生动物是宝贵的自然资源，在生态系统中发挥着重要作用1，开展长期、精准且系统的野生动物监测对于实施科学的保护策略具有重要意义。（剩余14921字）