The problem of food segmentation is quite challenging since food is characterized by intrinsic high intra-class variability. Also, segmentation of food images taken in-the-wild may be characterized by acquisition artifacts, and that could be problematic for the segmentation algorithms. A proper evaluating of segmentation algorithms is of paramount importance for the design and improvement of food analysis systems that can work in less-than-ideal real scenarios. In this paper, we evaluate the performance of different deep learning-based segmentation algorithms in the context of food. Due to the lack of large-scale food segmentation datasets, we initially create a new dataset composed of 5000 images of 50 diverse food categories. The images are accurately annotated with pixel-wise annotations. In order to test the algorithms under different conditions, the dataset is augmented with the same images but rendered under different acquisition distortions that comprise illuminant change, JPEG compression, Gaussian noise, and Gaussian blur. The final dataset is composed of 120,000 images. Using standard benchmark measures, we conducted extensive experiments to evaluate ten state-of-the-art segmentation algorithms on two tasks: food localization and semantic food segmentation.

食品细分的问题非常具有挑战性，因为食品具有内在的高内在类别变异性的特征。而且，野外拍摄的食物图像的分割可能会以采集伪像为特征，这对于分割算法可能是有问题的。正确评估分割算法对于设计和改进可以在不理想的实际情况下工作的食品分析系统至关重要。在本文中，我们评估了在食品环境中不同的基于深度学习的分割算法的性能。由于缺乏大规模的食品细分数据集，我们最初创建了一个新的数据集，其中包含50种不同食品类别的5000张图像。使用逐像素注释对图像进行精确注释。为了在不同条件下测试算法，数据集将使用相同的图像进行扩充，但是会在不同的采集失真下进行渲染，这些采集失真包括光源变化，JPEG压缩，高斯噪声和高斯模糊。最终数据集由120,000张图像组成。我们使用标准的基准测试方法，进行了广泛的实验，以评估两项最新任务的十种最新分割算法：食物定位和语义食物分割。