Considering the high incidence of accidents at tunnel construction sites, using robots to replace humans in hazardous tasks can effectively safeguard their lives. However, most robots currently used in this field require manual control and lack autonomous obstacle avoidance capability. To address these issues, we propose a lightweight model based on an improved version of YOLOv5 for obstacle detection. Firstly, to enhance detection speed and reduce computational load, we modify the backbone network to the lightweight Shufflenet v2. Secondly, we introduce a coordinate attention mechanism to enhance the network’s ability to learn feature representations. Subsequently, we replace the neck convolution block with GSConv to improve the model’s efficiency. Finally, we modify the model’s upsampling method to further enhance detection accuracy. Through comparative experiments on the model, the results demonstrate that our approach achieves an approximately 37% increase in detection speed with a minimal accuracy reduction of 1.5%. The frame rate has improved by about 54%, the parameter count has decreased by approximately 74%, and the model size has decreased by 2.5 MB. The experimental results indicate that our method can reduce hardware requirements for the model, striking a balance between detection speed and accuracy.

In this paper, we first recapitulate some basic notions of the CO ${}_2$ sequestration and numerical model. Next, a mixed model is employed into the CO ${}_2$ sequestration framework, for simulating CO ${}_2$ geological sequestration processes. The last part of the paper makes extensions to evaluation of the effectiveness of CO ${}_2$ sequestration with respect to atmospheric pressure, formation temperature, the initial reactant concentration, fracture aperture, and fracture dip. The results show that reactive Portland cement has a great impact on the effectiveness of CO ${}_2$ sequestration, while the proposed mixed model is robust in simulation.