用新数据集和改进模型提升复杂环境下的果蔬检测精度
Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel Dataset
- 构建67类果蔬的非受限场景数据集，结合半监督标注生成边界框
- 提出FVDNet模型，mAP达0.78，在小目标检测上表现优异
- 适合农业自动化、智能分拣等实际应用场景
利用计算机视觉自动化检测水果和蔬菜对现代化农业至关重要，可提升效率、保障食品质量，并推动技术先进与可持续的农业生产。本文提出一个端到端的实时场景下果蔬检测与定位流程。为此，我们构建了名为FRUVEG67的数据集，包含67类果蔬在非受限环境中的图像，每类仅含少量人工标注样本。我们开发了一种半监督数据标注算法（SSDA），用于为未标注图像生成目标边界框。检测方面，提出果蔬检测网络（FVDNet），它是YOLOv7的集成版本，采用三种不同的网格配置。通过平均边界框预测和类别投票机制提升结果可靠性。同时引入焦损失与詹森-香农散度（JSD）结合，增强小目标检测能力。实验表明，FVDNet显著优于先前版本的YOLO，整体平均精度（mAP）达到0.78。此外，我们在开放类冰箱图像上评估了FVDNet，结果表现出色。
原文摘要 · Abstract (English)
Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results.
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