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RepMSNet: multi-scale convolutional neural network combined with structural re-parameterization for the classification of agricultural pests
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DOI：10.1007/s11760-026-05292-8.png)
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Abstract
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To address the limitations of convolutional neural networks (CNNs) in current agricultural pest classification tasks, particularly their insufficient performance and lack of multi-scale feature extraction capability, this paper proposes a novel CNN architecture named RepMSNet. By employing parallel convolutions with varying kernel sizes, RepMSNet enhances multi-scale feature extraction. Additionally, structural re-parameterization is introduced to streamline the model architecture, further improving inference speed. Experimental results on the IP102 dataset demonstrate that RepMSNet achieves a classification accuracy of 72.0%, which rises to76.3%after ImageNet-1 K pre-training and fine-tuning, surpassing existing single-model methods based on CNNs or Vision Transformers (ViTs). In terms of inference speed, RepMSNet reaches 134 FPS, outperforming ViT-based approaches and most CNN-based methods. Ablation studies provide a detailed analysis of the rationale behind each design component of RepMSNet. All model code and trained weights are open-sourced at: https://github.com/OnlyForWW/WorkForIP102.
Keywords:
Agricultural pest classification
Convolutional neural network (CNN)
Multi-scale feature extraction
Structural re-parameterization
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