Bio-DETR: A Transformer-based Network for Pest and Seed Detection with Hyperspectral Images

• Published in: 2024 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
• Main Authors: Di, Yang, Phung, Son Lam, Van Den Berg, Julian, Clissold, Jason, Bui, Ly, Le, Hoang Thanh, Bouzerdoum, Abdesselam
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30.06.2024
• Subjects: Feature extraction; hyperspectral imaging; Neural networks; Object detection; Pest detection; Production; Real-time systems; seed recognition; Seeds (agriculture); self-attention mechanism; Transformers; vision transformer
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN60899.2024.10650195

Exotic pests and seeds pose a serious threat to agricultural production and ecosystems, leading to significant labour and economic losses. Therefore, automated pest and seed detection systems are crucial for biosecurity and agriculture. Biosecurity detection systems need to identify pests and seeds of various sizes and types in a variable and complex environment, making high-precision automated detection a challenging task. To address this, we propose Bio-DETR, a lightweight transformer-based architecture for accurate pest and seed detection. We introduce two self-attention-based modules, Hybrid Scale Attention and Dynamic Bilateral Attention, for enhanced feature extraction and multiscale information fusion. The effectiveness of these modules is validated experimentally. We also propose HSI-Bio, a large-scale dataset with 8,000 images across 23 categories, collected using a hyperspectral camera on diverse backgrounds. Compared to RGB images, hyperspectral images (HSI) offer rich channel information. The representative spectra are selected from HSI for experiments. Bio-DETR achieves an AP 50 of 87.4% and an AP of 62.2% on HSI-Bio, outperforming other state-of-the-art methods and achieving real-time detection of 52 FPS. Our code is available at: https://github.com/yangdi-cv/Bio-DETR.