自动化处理海量甲虫图像，提升生物研究效率
BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing
- 三阶段流水线：检测、裁剪、形态分割
- 基于视觉语言模型实现开集目标检测，准确率高
- 专为甲虫设计，适合生态与分类学研究
在昆虫学与生态学研究中，生物学家常需采集大量昆虫，其中甲虫最为常见。通常做法是将甲虫置于托盘上拍摄，面对成千上万张托盘图像，亟需自动化处理流程以支持后续研究。为此，我们构建了一个三阶段深度学习流水线：首先检测托盘中所有甲虫，其次对每只甲虫进行排序与裁剪，最后对裁剪后的图像进行形态分割。检测阶段采用基于Transformer的开集目标检测器与视觉-语言模型协同迭代；分割阶段人工标注670张甲虫图像，微调两种Transformer分割模型，实现细粒度分割且精度较高。该流程整合多种深度学习方法，专为甲虫图像处理优化，显著提升大规模数据处理效率，加速生物学研究进程。
原文摘要 · Abstract (English)
In entomology and ecology research, biologists often need to collect a large number of insects, among which beetles are the most common species. A common practice for biologists to organize beetles is to place them on trays and take a picture of each tray. Given the images of thousands of such trays, it is important to have an automated pipeline to process the large-scale data for further research. Therefore, we develop a 3-stage pipeline to detect all the beetles on each tray, sort and crop the image of each beetle, and do morphological segmentation on the cropped beetles. For detection, we design an iterative process utilizing a transformer-based open-vocabulary object detector and a vision-language model. For segmentation, we manually labeled 670 beetle images and fine-tuned two variants of a transformer-based segmentation model to achieve fine-grained segmentation of beetles with relatively high accuracy. The pipeline integrates multiple deep learning methods and is specialized for beetle image processing, which can greatly improve the efficiency to process large-scale beetle data and accelerate biological research.
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