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End-to-end learning for image-based air quality level estimation

机译：端到端学习以基于图像的空气质量水平估算

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Air quality estimation is an important and fundamental problem in environmental protection. Several efforts have been made in the past decades using expensive sensor-based or indirect methods like based on social networks; however, image-based air pollution estimation is still far from solved. This paper devises an effective convolutional neural network (CNN) to estimate air quality based on images. Our method is comprised of three ingredients: We first design an ensemble CNN for air quality estimation which is expected to obtain more accurate and stable results than a single classifier. Second, three ordinal classifiers, namely negative log–log ordinal classifier, cauchit ordinal classifier and complementary log–log ordinal classifier, are devised in the last layer of each CNN, to improve the ordinal discriminative ability of the model. Third, as a variant of the rectified linear units, an adjusted activation function is introduced. We collect open air images with corresponding air quality levels from an official agency as the ground truth. Experimental results demonstrate the effectiveness of our method on the real-world dataset.
机译：空气质量评估是环境保护中的一个重要而根本的问题。在过去的几十年中，已经使用基于传感器的昂贵方法或基于社交网络的间接方法做出了一些努力。然而，基于图像的空气污染估算仍远未解决。本文设计了一种有效的卷积神经网络（CNN），以基于图像估算空气质量。我们的方法包括三个要素：我们首先设计一个用于空气质量估算的整体CNN，与单个分类器相比，该CNN有望获得更准确和稳定的结果。其次，在每个CNN的最后一层设计了三个序数分类器，即负对数序数对数分类器，柯西序数分类器和互补对数序数对数分类器，以提高模型的序数判别能力。第三，作为整流线性单元的变体，引入了调整后的激活函数。我们从官方机构收集具有相应空气质量水平的露天图像作为地面实况。实验结果证明了我们的方法在真实数据集上的有效性。

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