提出首个综合评估害虫计数置信度的方法，提升农业监测可靠性。
Counting with Confidence: Accurate Pest Monitoring in Water Traps
- 融合计数结果与图像质量、分布均匀性等多因素评估置信度。
- 在测试集上使均方误差降低31.7%，决定系数提升15.2%。
- 适合需要高可信度计数的智能农业场景使用。
精准的害虫种群监测及其动态变化跟踪对精准农业决策至关重要。现有基于视觉的自动害虫计数研究通常在具有真实标签的数据集上评估模型，但在实际部署中缺乏对计数结果可靠性的评估，因真实标签不可得。为此，本文提出一种全面评估图像中害虫计数置信度的方法，结合计数结果相关信息与外部环境条件。首先，利用害虫检测网络进行害虫检测与计数，并提取计数相关特征；其次，对害虫图像进行图像质量评估、图像复杂度评估以及害虫分布均匀性评估；通过计算平均梯度幅值量化采集中搅动引起的图像清晰度变化。值得注意的是，我们设计了一种假设驱动的多因素敏感性分析方法，以选择最优的图像质量与复杂度评估方法；并提出一种自适应DBSCAN聚类算法用于分布均匀性评估。最后，将获取的计数相关与环境条件信息输入回归模型，预测最终的害虫计数置信度。据我们所知，这是首个致力于全面评估计数任务置信度的研究，并通过模型量化影响因素与置信度之间的关系。实验结果表明，相较于仅依赖计数相关信息构建的基线模型，本方法在害虫计数置信度测试集上降低了31.7%的均方误差（MSE），并提升了15.2%的决定系数（R²）。
原文摘要 · Abstract (English)
Accurate pest population monitoring and tracking their dynamic changes are crucial for precision agriculture decision-making. A common limitation in existing vision-based automatic pest counting research is that models are typically evaluated on datasets with ground truth but deployed in real-world scenarios without assessing the reliability of counting results due to the lack of ground truth. To this end, this paper proposed a method for comprehensively evaluating pest counting confidence in the image, based on information related to counting results and external environmental conditions. First, a pest detection network is used for pest detection and counting, extracting counting result-related information. Then, the pest images undergo image quality assessment, image complexity assessment, and pest distribution uniformity assessment. And the changes in image clarity caused by stirring during image acquisition are quantified by calculating the average gradient magnitude. Notably, we designed a hypothesis-driven multi-factor sensitivity analysis method to select the optimal image quality assessment and image complexity assessment methods. And we proposed an adaptive DBSCAN clustering algorithm for pest distribution uniformity assessment. Finally, the obtained information related to counting results and external environmental conditions is input into a regression model for prediction, resulting in the final pest counting confidence. To the best of our knowledge, this is the first study dedicated to comprehensively evaluating counting confidence in counting tasks, and quantifying the relationship between influencing factors and counting confidence through a model. Experimental results show our method reduces MSE by 31.7% and improves R2 by 15.2% on the pest counting confidence test set, compared to the baseline built primarily on information related to counting results.
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