EBGC-RTDETR: A lightweight model for eggplant disease detection with enhanced RT-DETR architecture
Year: 2025
Type: article
Abstract: • Efficient Channel Attention (ECA) enhances lesion feature extraction under occlusion and uneven illumination. • GSConv combined with BiFPN enables efficient multi-scale feature fusion, improving detection accuracy for large and small lesions. • Group_Taylor pruning with CWD-based channel knowledge distillation reduces parameters and computational cost while maintaining high precision. • The EBGC-RTDETR model achieves a 51.5% reduction in parameters, 67.2% decrease in FLOPs, and improves [email protected] by 2.3% and F1 score by 1.23% compared with RT-DETR. To address the limitations in lesion characterization, regional attention, multi-scale feature fusion, and lightweight deployment in existing models, we propose EBGC-RTDETR, an improved architecture based on RT-DETR-r18. First, an Efficient Channel Attention (ECA) module is added after the final BasicBlock in the backbone to enhance lesion representation and alleviate insufficient feature extraction. Second, this study designs a Ghost-Shuffle Convolution (GSConv) based Bidirectional Feature Pyramid Network (GS-BiFPN), which integrates the lightweight GSConv modules with a bidirectional feature pyramid to replace the original encoder neck, enabling efficient multi-scale feature fusion. Finally, Group-Level First-Order Taylor (Group-Taylor) pruning and Channel-Wise Distillation (CWD) are applied to remove redundant structures and reduce computational cost while preserving essential feature representations. Experimental results show that EBGC-RTDETR achieves notable improvements over RT-DETR-r18: Precision increases by 0.6%, Recall by 1.7%, mean average precision (mAP0.5) by 2.3%, mAP0.5-0.95 by 1.6%, and F1 Score by 1.2%. Meanwhile, the number of parameters and computational complexity (GFLOPs) are reduced by 51.5% and 67.2%, respectively. The proposed model achieves both enhanced performance and lightweight design, providing a valuable theoretical and practical reference for machine vision applications in eggplant disease detection.
Source: Smart Agricultural Technology
Authors: Zengguang Huo, Xinyu Yang, Xinghua Sun, Bei Zhang, Dan Liu
Institution: Hebei North University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,770
Award: G287344767
OpenAlex ID: W7117588967