A Shallow ResNet with Layer Enhancement for Image-Based Particle Pollution Estimation

• Published in: Pattern Recognition and Computer Vision Vol. 11257; pp. 381 - 391
• Main Authors: Yang, Wenwen, Feng, Jun, Bo, Qirong, Yang, Yixuan, Jiang, Bo
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Image enhancement; Layer enhancement; Particulate matter; Shallow ReNet
• ISBN: 9783030033347; 3030033341
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-03335-4_33

Airborne particle pollution especially matter with a diameter less than 2.5 μm (PM2.5) has become an increasingly serious problem and caused grave public health concerns. An easily and reliable accessible method to monitor the particles can greatly help raise public awareness and reduce harmful exposures. In this paper, we proposed a shallow ResNet with layer enhancement for PM2.5 Index Estimation, called PMIE. An inter-layer weights discrimination of convolutional neural networks method is proposed, providing a meaningful reference for CNN’s design. In addition, a new method for enhancing the effect of the convolution layer was first introduced and was applied under the guidance of the CNN inter-layer weights discrimination method we proposed. This shallow ResNet consists of seven residual blocks with last two layer enhancements. We assessed our method on two datasets collected from Shanghai City and Beijing City in China, and compared with the state-of-the-art. For Shanghai dataset, PMIE reduced RMSE by 11.8% and increased R-squared by 4.8%. For Beijing dataset, RMSE is reduced by 14.4% and R-squared is increased by 23.6%. The results demonstrated that the proposed method PMIE outperforming the state-of-the-art for PM2.5 estimation.