蜂类和黄蜂分层巢穴陷阱中高效育雏细胞检测：平衡标注工作量与物种覆盖度
Efficient Brood Cell Detection in Layer Trap Nests for Bees and Wasps: Balancing Labeling Effort and Species Coverage
AI总结：
提出基于深度学习的育雏细胞检测与分类方法，通过约束假阳性损失策略减少标注工作量并缓解类别不平衡，提升检测性能。
摘要：
监测洞穴筑巢的野生蜂类和黄蜂对生物多样性研究和保护至关重要。分层巢穴陷阱（LTNs）正成为研究这些昆虫丰度和物种丰富度的宝贵工具，可深入了解其筑巢活动和生态需求。然而，手动评估LTNs以检测和分类育雏细胞既费时又费力。为此，我们提出一种基于深度学习的方法，用于高效检测和分类LTNs中的育雏细胞。LTNs由于育雏细胞密集排列，导致每张图像的标注工作量很高。此外，我们观察到类别分布显著不平衡，常见物种的出现次数明显多于稀有物种。对常见物种进行全面标注既耗时又加剧数据不平衡，而部分标注则导致数据不完整，从而降低模型性能。为了减少标注工作量并减轻未标注数据的影响，我们引入了一种新颖的约束假阳性损失（CFPL）策略。CFPL动态屏蔽未标注数据的预测，防止其在训练过程中干扰分类损失。实验结果表明，我们的方法提高了检测性能，平衡了模型准确性和标注工作量，同时缓解了类别不平衡问题。
英文摘要：
Monitoring cavity-nesting wild bees and wasps is vital for biodiversity research and conservation. Layer trap nests (LTNs) are emerging as a valuable tool to study the abundance and species richness of these insects, offering insights into their nesting activities and ecological needs. However, manually evaluating LTNs to detect and classify brood cells is labor-intensive and time-consuming. To address this, we propose a deep learning based approach for efficient brood cell detection and classification in LTNs. LTNs present additional challenges due to densely packed brood cells, leading to a high labeling effort per image. Moreover, we observe a significant imbalance in class distribution, with common species having notably more occurrences than rare species. Comprehensive labeling of common species is time-consuming and exacerbates data imbalance, while partial labeling introduces data incompleteness which degrades model performance. To reduce labeling effort and mitigate the impact of unlabeled data, we introduce a novel Constrained False Positive Loss (CFPL) strategy. CFPL dynamically masks predictions from unlabeled data, preventing them from interfering with the classification loss during training. Experimental results demonstrate that our method improves detection performance, balances model accuracy and labeling effort, while also mitigating class imbalance.
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