Estimation of coal dust parameters via an effective image-based deep learning model

In high-pressure transportation, characterizing the leakage status of coal dust is an effective means to reduce potential safety hazards in the optimization of energy structures, and it is also conducive to disaster prevention and safety management. Given the existing methods, manual inspection of leakage points requires high measurement skills, entails significant maintenance costs, and is time-consuming and challenging. Therefore, a synergetic network structure based on an instance segmentation, integrated with multiregression models, is proposed. This model is used to study the detailed characteristics of complex coal particles and estimate coal dust parameters, providing a practical means for onsite environmental assessment. First, a cascade mechanism of ghost convolution and a depthwise split attention module is added to the backbone network to reduce the number of network parameters and improve the channel correlation of coal dust images. Second, the multiscale feature pyramid network structure is introduced to increase low-level feature information in coal dust images and enhance attention to small particle characteristics of coal dust. Moreover, the head structure of the segmentation branch is optimized via the parameter-free attention module to improve mask precision. Finally, the optimized elastic network fusion model is used to estimate multiple regression coal dust parameters. The experimental results show that the proposed model outperforms the other models in terms of segmentation accuracy, the intersection ratio, and the recall ratio. The average error in the mass distribution characterization is less than ±10 %, which meets the theoretical expectations. An ideal balance is achieved between computational speed and segmentation accuracy.