Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer
Ruikang Xu, Jiajun Yu, Lening Ai, Haojie Yu, Zining Wei
TL;DR
This paper introduces a new method combining Cascade RCNN and Swin Transformer to accurately detect and identify farmland pests using improved feature extraction and data augmentation techniques.
Contribution
A novel two-stage pest detection method combining Cascade RCNN and Swin Transformer with SCF-FPN module for enhanced feature extraction.
Findings
The proposed method achieved 92.5% accuracy, 91.8% recall, and 93.7% mAP in detecting 28 pest species.
The model outperformed the baseline by 12.1% in accuracy, 5.4% in recall, and 7.6% in mAP.
The SCF-FPN module and data augmentation techniques significantly improved pest detection performance.
Abstract
Agricultural pests and diseases pose major losses to agricultural productivity, leading to significant economic losses and food safety risks. However, accurately identifying and controlling these pests is still very challenging due to the scarcity of labeling data for agricultural pests and the wide variety of pest species with different morphologies. To this end, we propose a two-stage target detection method that combines Cascade RCNN and Swin Transformer models. To address the scarcity of labeled data, we employ random cut-and-paste and traditional online enhancement techniques to expand the pest dataset and use Swin Transformer for basic feature extraction. Subsequently, we designed the SCF-FPN module to enhance the basic features to extract richer pest features. Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive…
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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Geographic Information Systems Studies