It is generally accepted that one of the critical parts of current vision algorithms based on deep learning and convolutional neural networks is the annotation of a sufficient number of images to achieve competitive performance. This is particularly difficult for semantic segmentation tasks since the annotation must be ideally generated at the pixel level. Weakly-supervised semantic segmentation aims at reducing this cost by employing simpler annotations that, hence, are easier, cheaper and quicker to produce. In this paper, we propose and assess a new weakly-supervised semantic segmentation approach making use of a novel loss function whose goal is to counteract the effects of weak annotations. To this end, this loss function comprises several terms based on partial cross-entropy losses, being one of them the Centroid Loss. This term induces a clustering of the image pixels in the object classes under consideration, whose aim is to improve the training of the segmentation network by guiding the optimization. The performance of the approach is evaluated against datasets from two different industry-related case studies: while one involves the detection of instances of a number of different object classes in the context of a quality control application, the other stems from the visual inspection domain and deals with the localization of images areas whose pixels correspond to scene surface points affected by a specific sort of defect. The detection results that are reported for both cases show that, despite the differences among them and the particular challenges, the use of weak annotations do not prevent from achieving a competitive performance level for both.
翻译:人们普遍认为,基于深层学习和进化神经网络的当前愿景算法的关键部分之一是说明足够数量的图像,以达到竞争性性能。由于语义分解任务必须最好在像素水平上生成,因此对于语义分解任务特别困难,因为语义分解任务必须最好在像素水平上生成。由微弱监督的语义分解,目的是通过使用更简单的说明来降低这一成本,从而更加容易、更便宜和更快地生成。在本文中,我们建议和评估一种新的竞争性语义分解方法,利用一种新式的薄弱、受监督的语义分解方法,目的是抵消微弱的语义分解功能。为此,这一损失函数包含基于部分交叉性损失的几个术语,即中度损失。这个术语导致将图像像像素组合在所审议的对象类别中,目的是通过引导优化来改进对分解网络的培训。根据两个不同行业案例研究的数据集评估了方法的绩效:一个是检测到不同对象类别中的一些实例,而另一个则是在特定质量控制范围内的实地检查结果。