使用主要合成图像为商业烘焙坊训练计算机视觉模型
Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images
AI总结：
本文通过构建含2432张图像的扩展数据集，并利用pix2pix和CycleGAN生成合成图像增强鲁棒性，训练YOLOv9与YOLOv8进行商业烘焙食品检测，最佳模型AP@0.5达90.3%。
摘要：
在食品工业中，对退回产品进行再处理是提高资源效率的关键步骤。[SBB23]提出了一种人工智能应用，可自动追踪退回的面包卷。我们通过构建一个包含2432张图像且涵盖更广泛烘焙食品的扩展数据集来拓展其工作。为提高模型鲁棒性，我们使用生成模型pix2pix和CycleGAN创建合成图像。我们在检测任务上训练了最先进的目标检测模型YOLOv9和YOLOv8。我们整体表现最佳的模型在测试集上实现了90.3%的平均精度AP@0.5。
英文摘要：
In the food industry, reprocessing returned product is a vital step to increase resource efficiency. [SBB23] presented an AI application that automates the tracking of returned bread buns. We extend their work by creating an expanded dataset comprising 2432 images and a wider range of baked goods. To increase model robustness, we use generative models pix2pix and CycleGAN to create synthetic images. We train state-of-the-art object detection model YOLOv9 and YOLOv8 on our detection task. Our overall best-performing model achieved an average precision AP@0.5 of 90.3% on our test set.
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。
