A Shallow ResNet with Layer Enhancement for Image-Based Particle Pollution Estimation
Airborne particle pollution especially matter with a diameter less than 2.5 lm (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.
Particulate matter Image enhancement Shallow ReNet Layer enhancement
Wenwen Yang Jun Feng Qirong Bo Yixuan Yang Bo Jiang
Northwest University,Xian 710127,China
国际会议
广州
英文
381-391
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）