BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing
This work addresses the need for efficient beetle data processing in biological research, but it is incremental as it integrates existing deep learning methods for a specific domain.
The authors tackled the problem of automating large-scale beetle image processing for entomology and ecology research by developing a 3-stage pipeline that detects, crops, and segments beetles, achieving relatively high accuracy with manual labeling of 670 images.
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.