用深度学习实现蜂群快速精准计数，1秒处理一张图。
Fast, accurate measurement of the worker populations of honey bee colonies using deep learning
- 用密度图估计法预测蜂群数量，解决重叠遮挡问题。
- CSRNet每图仅需1秒，复杂场景下计数准确率高。
- 适合生态研究者和养蜂人做大规模蜂群监测。
蜜蜂在授粉中起关键作用，对全球农业和生态系统至关重要。准确估算蜂巢种群数量对理解环境因素影响至关重要，但传统人工计数耗时费力且易出错，尤其在大规模研究中。本文提出基于深度学习的蜂群计数方法，采用CSRNet模型，并构建了首个专为此任务设计的高分辨率数据集ASUBEE。方法通过密度图估计预测蜂群数量，有效应对蜂群密集、重叠遮挡等挑战。实验表明，CSRNet每张图像仅需1秒计算时间，在复杂密集场景中仍保持高精度。结果证明，深度学习技术可显著提升蜂群评估效率，为研究人员与养蜂人提供高效精准的监测工具。本工作推动了人工智能在生态研究中的应用，实现了可扩展、高精度的蜂群监控。
原文摘要 · Abstract (English)
Honey bees play a crucial role in pollination, contributing significantly to global agriculture and ecosystems. Accurately estimating hive populations is essential for understanding the effects of environmental factors on bee colonies, yet traditional methods of counting bees are time-consuming, labor-intensive, and prone to human error, particularly in large-scale studies. In this paper, we present a deep learning-based solution for automating bee population counting using CSRNet and introduce ASUBEE, the FIRST high-resolution dataset specifically designed for this task. Our method employs density map estimation to predict bee populations, effectively addressing challenges such as occlusion and overlapping bees that are common in hive monitoring. We demonstrate that CSRNet achieves superior performance in terms of time efficiency, with a computation time of just 1 second per image, while delivering accurate counts even in complex and densely populated hive scenarios. Our findings show that deep learning approaches like CSRNet can dramatically enhance the efficiency of hive population assessments, providing a valuable tool for researchers and beekeepers alike. This work marks a significant advancement in applying AI technologies to ecological research, offering scalable and precise monitoring solutions for honey bee populations.
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