Semmeldetector：机器学习在商业面包店中的应用
Semmeldetector: Application of Machine Learning in Commercial Bakeries
AI总结：
本文提出 Semmeldetector，利用 YOLOv8 和 Copy-Paste 增强在 1151 张图像、18 类烘焙食品上实现检测计数，最佳模型 AP@0.5 达 89.1%，证明小数据集下机器学习可有效应用于面包店等非传统行业。
摘要：
Semmeldetector 是一个机器学习应用，利用目标检测模型对图像中的烘焙食品进行检测、分类和计数。该应用使商业烘焙商能够跟踪未售出的烘焙食品，从而优化生产并提高资源效率。我们编制了一个包含 1151 张图像的数据集，区分 18 种不同类型的烘焙食品，用于训练检测模型。为便于模型训练，我们使用了 Copy-Paste 增强流水线来扩展数据集。我们在检测任务上训练了最先进的目标检测模型 YOLOv8。我们测试了不同训练数据、模型规模以及在线图像增强流水线对模型性能的影响。我们整体表现最佳的模型在测试集上达到了 89.1% 的 AP@0.5。基于这些结果，我们得出结论：即使在数据集非常有限的情况下，机器学习对于面包店等意想不到的行业来说，也可以成为一种有价值的工具。
英文摘要：
The Semmeldetector, is a machine learning application that utilizes object detection models to detect, classify and count baked goods in images. Our application allows commercial bakers to track unsold baked goods, which allows them to optimize production and increase resource efficiency. We compiled a dataset comprising 1151 images that distinguishes between 18 different types of baked goods to train our detection models. To facilitate model training, we used a Copy-Paste augmentation pipeline to expand our dataset. We trained the state-of-the-art object detection model YOLOv8 on our detection task. We tested the impact of different training data, model scale, and online image augmentation pipelines on model performance. Our overall best performing model, achieved an AP@0.5 of 89.1% on our test set. Based on our results, we conclude that machine learning can be a valuable tool even for unforeseen industries like bakeries, even with very limited datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
