Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (20): 232-242.

### Improving YOLOv3 Network to Assess Air Quality in Image

DENG Yinong, LUO Jianxin, ZHANG Qi, LIU Zhen, HU Qi, JIN Fenglin, BI Pengcheng

1. 1.College of Command & Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
2.Nanjing Lucky Dog Network Technology Co., Ltd., Nanjing 210000, China
• Online:2020-10-15 Published:2020-10-13

### 改进YOLOv3网络在图像中评价空气质量

1. 1.陆军工程大学 指挥控制工程学院，南京 210007
2.南京小吉狗网络科技有限公司，南京 210000

Abstract:

Air quality index and PM2.5 concentration are two important indicators to measure the degree of air pollution. In the framework of image detection algorithm, this paper proposes an air quality assessment method based on YOLOv3 network model. The YOLOv3 network is improved in the model, which includes two modules：multi-anchor detection mechanism and convolution voting network. The model is suitable for air quality index evaluation in non-fixed scenarios and PM2.5 concentration prediction in fixed scenarios. The accuracy of the method described in this paper has been ranked 3rd in the global AI application contest in 2018. At the same time, the YOLOv3 model based on Darknet framework can meet the real-time requirements, which has important reference significance for the related research of air quality assessment.