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This work aims to enhance insect classification accuracy using an innovative data augmentation technique.

April 25, 2026Open Access

Shape Morphing for Data Augmentation : An Innovative Approach for Enhancing Insect Classification

Key Points

This work aims to enhance insect classification accuracy using an innovative data augmentation technique.
Introduced a morphing method using ARAP deformation and landmark detection for data augmentation.
Utilized geometric warping and texture interpolation to create synthetic images of insects.
Validated on Leeds Butterfly Dataset and Butterfly Moths Image Classification Dataset with InceptionV3 network.
Achieved significant improvement in classification performance metrics: precision, recall, accuracy, and F1-score.
Outperformed traditional techniques like Mixup, Cutout, and Rotation in enhancing insect classification.

Abstract

This work introduces an innovative intra-class morphing data augmentation method designed to improve insect classification in low-data scenarios. Leveraging As-Rigid-As- Possible (ARAP) deformation and landmark detection, the approach combines controlled geometric warping with realistic texture interpolation to generate synthetic images that remain faithful to species-specific morphological characteristics. The method was rigorously validated, both qualitatively and quantitatively, on the Leeds Butterfly Dataset and the Butterfly Moths Image Classification (100 Species) Dataset. When integrated into an InceptionV3 network pre-trained on ImageNet, the proposed augmentation leads to a significant improvement in classification performance, consistently outperforming traditional techniques such as Mixup, Cutout, and Rotation across key metrics, including precision, recall, accuracy, and F1-score.

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