An efficient firefighting method for robotics: A novel convolution-based lightweight network model guided by contextual features with dual attention

Efficient firefighting operations are crucial for ensuring the safety of firefighters and preventing direct exposure to high-temperature and high-radiation environments. However, traditional firefighting robots face the challenges of low efficiency, high misjudgment rates, and difficulty in control during firefighting processes, particularly in extremely complex and dynamically changing fire scenes. Therefore, this article proposes a novel convolution-based context-guided dual attention lightweight network (CG-DALNet) model to develop efficient firefighting methods for firefighting robots. To expand the field of fire perception, this study employs monocular vision from drones to assist ground firefighting robots in autonomous firefighting decision-making in an end-to-end manner. By introducing depthwise separable convolutions to construct the feature backbone layer, the number of the parameters in the model is reduced. To better understand target position information in fire scenes, we propose a position attention module guided by contextual features to enhance the model's positional awareness. Additionally, to efficiently integrate feature information at different scales in the fire scene, we adopt a residual-connected convolutional kernel attention module to enhance the model's ability to express complex fire scene features. Numerical experiments show that the proposed CG-DALNet lightweight network model achieves significant performance improvement in autonomous firefighting tasks for robots. This research provides an innovative solution for autonomous firefighting methods for firefighting robots and demonstrates its effectiveness and potential.