用视觉技术检测甜菜收获后存储问题，提升糖业质检效率
SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets
- 两阶段方法：先检测甜菜，再分割损伤、腐烂等缺陷
- 检测精度达mAP50-95 98.8，分割模型最佳mIoU为64.0
- 适用于农业质检、智能加工场景，尤其关注存储损耗
甜菜在加工前储存过程中会因附着土壤中的微生物和多余植株而损失糖分。本研究提出一种新型高质量标注数据集与两阶段方法，用于在单目RGB图像中检测、语义分割及估算收获后和储存后甜菜的数量与质量。针对甜菜检测及细粒度语义分割（包括损伤、腐烂、土壤附着和多余植被），我们进行了大量消融实验，评估了多种图像尺寸、模型架构、编码器以及环境条件的影响。实验结果显示，甜菜检测的mAP50-95达到98.8，最佳分割模型的mIoU为64.0。
原文摘要 · Abstract (English)
While sugar beets are stored prior to processing, they lose sugar due to factors such as microorganisms present in adherent soil and excess vegetation. Their automated visual inspection promises to aide in quality assurance and thereby increase efficiency throughout the processing chain of sugar production. In this work, we present a novel high-quality annotated dataset and two-stage method for the detection, semantic segmentation and mass estimation of post-harvest and post-storage sugar beets in monocular RGB images. We conduct extensive ablation experiments for the detection of sugar beets and their fine-grained semantic segmentation regarding damages, rot, soil adhesion and excess vegetation. For these tasks, we evaluate multiple image sizes, model architectures and encoders, as well as the influence of environmental conditions. Our experiments show an mAP50-95 of 98.8 for sugar-beet detection and an mIoU of 64.0 for the best-performing segmentation model.
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