Author:
Su Qiao,Wang Hongsu,Zhao Haiyang,Chu Yan,Li Jie,Lyu Xuan,Li Zijuan
Abstract
AbstractAn edge-cloud computing collaborative dust concentration detection architecture is proposed for real-time operation of intelligent algorithms to reduce the warning delay. And, an end-to-end three-channel convolutional neural network (E2E-SCNN) method is proposed in the paper to facilitate intelligent monitoring and management of dust concentration in tobacco production workshops. This model, which includes three sub-networks-a local feature branch, a global feature branch, and a spatial feature branch, learns the detail texture, overall layout, and spatial distribution information of the input image respectively. A fusion of the three complementary features is performed at the end of the network for the final dust concentration regression prediction. The design, when compared with the single network structure that directly regresses the entire image, is shown to more fully represent the overall information of the image and enhance monitoring performance. A richly annotated image dataset of tobacco production workshops is constructed to verify the effectiveness of the proposed method. The prediction error of E2E-SCNN is compared with existing image estimation algorithms, dual-channel networks, and other methods on this dataset using indicators such as Mean Absolute Error (MAE) and $${R}^{2}$$
R
2
. It is shown by the results that excellent performance is achieved by the E2E-SCNN algorithm, significantly surpassing other comparison methods. The paper demonstrates that the accuracy and robustness of dust concentration prediction can be greatly improved by using a three-channel convolutional neural network spatial information monitoring framework. This achievement provides an effective means for dust supervision and governance during the tobacco production process and offers a technical route that can be referred to for image analysis tasks in other similar fields.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Reference26 articles.
1. Zaga V, Dell’Omo M, Murgia N et al (2021) Tobacco worker’s lung: A neglected subtype of hypersensitivity pneumonitis. Lung 199:13–19
2. Patel J, Parmar R, Solanki H, et al (2023) Occupational Health Problems Among Tobacco Processing Factory Workers, at Kheda District Gujarat: A Cross Sectional Study. J Pharm Negat Results 1378–1387
3. Slobodyan O, Zaets V, Neschadym L, et al (2015) Cause of the fire at the food industry enterprises. Electronic National University of Food Technologies Institutional Repository 3(2):61–269
4. Mohammadyan M, Baharfar Y (2012) Evaluation of tobacco dust and designing of local exhaust ventilation (lev) systems in a tobacco processing industry. Int J Occup Hyg 4(1):47–52
5. Pinnick RG, Fernandez G, Hinds BD (1983) Explosion dust particle size measurements. Appl Opt 22(1):95–102