论文标题
使用对象检测和转移学习对啤酒瓶进行分类
Classification of Beer Bottles using Object Detection and Transfer Learning
论文作者
论文摘要
分类问题在计算机视觉中很常见。尽管如此，对于啤酒瓶的分类尚无专门的工作。作为大师赛深度学习的挑战的一部分，创建了5207个啤酒瓶图像和品牌标签的数据集。图像恰好包含一个啤酒瓶。在本文中，我们提出了一个深度学习模型，该模型将啤酒瓶的图片分为两步。作为第一步，更快的R-R-CNN检测到与品牌独立于分类相关的图像部分。在第二步中，相关图像部分由RESNET-18分类。置信度最高的图像部分将作为类标签返回。我们提出了一个模型，在最终测试数据集的挑战中，我们通过该模型超过了经典的一步转移学习方法，并达到了99.86％的精度。挑战结束后，我们能够达到100％的准确性
Classification problems are common in Computer Vision. Despite this, there is no dedicated work for the classification of beer bottles. As part of the challenge of the master course Deep Learning, a dataset of 5207 beer bottle images and brand labels was created. An image contains exactly one beer bottle. In this paper we present a deep learning model which classifies pictures of beer bottles in a two step approach. As the first step, a Faster-R-CNN detects image sections relevant for classification independently of the brand. In the second step, the relevant image sections are classified by a ResNet-18. The image section with the highest confidence is returned as class label. We propose a model, with which we surpass the classic one step transfer learning approach and reached an accuracy of 99.86% during the challenge on the final test dataset. We were able to achieve 100% accuracy after the challenge ended
