Garlic-YOLO-DD: a lightweight object detection algorithm for garlic damage detection

Abstract

To address the challenge of applying garlic damage detection models in resource-constrained environments, this study proposes Garlic-YOLO-DD—a lightweight single-stage object detection algorithm based on YOLOv11n. This model effectively resolves the core issues of high computational complexity and excessive parameters in existing methods, achieving efficient and accurate garlic damage recognition suitable for real-time applications. Specifically, replacing conventional convolutional modules in the backbone network with the ADown module significantly reduces parameters and computational load. Simultaneously, integrating the parameter-free SimAM attention mechanism enhances localization and feature extraction capabilities for subtle lesion areas. The efficient BiFPN architecture optimizes the original feature fusion network, improving both speed and effectiveness in multi-scale feature integration. Experiments conducted on a self-built garlic damage dataset demonstrate that the Garlic-YOLO-DD model reduces the number of parameters to 57.96% of YOLOv11n, decreases computational load by 20.63%, increases inference speed by 15.97%, and achieves mAP@50% by 27.64%. This study provides a computer vision solution for automated garlic damage detection in intelligent agricultural systems.