Fusion channel interaction attention network for water jet detection in firefighting robots

Abstract

Firefighting robots play a critical role in fire suppression. Ensuring the water stream precisely hits the target during autonomous fire extinguishing is of paramount importance. By visually detecting the landing point of the water jet, closed-loop control of the extinguishing process can be achieved. However, achieving accurate jet landing point localization in complex environments, such as changes in ambient lighting, jet end divergence, and jet breakup, presents a challenging task. To address this, we propose a novel CIA-YOLOX (Channel Interaction Attention–You Only Look Once) model for the precise identification of water jet landing points in firefighting robots using unmanned aerial vehicle (UAV) visual information. First, the model introduces the Triplet Attention (TA) mechanism to capture feature dependencies across different dimensions, enriching feature information. Second, a module named Coordinate Attention Transformer (CA-Trans) is designed to establish long-range dependencies between directional feature vectors, enabling the extraction of precise positional information critical for accurate impact point prediction. Additionally, a Dual-branch Channel Interactive Attention Fusion (DCIAF) module is proposed to enhance feature representation capabilities by facilitating feature complementation through semantic modeling of channel interactions. Experimental results indicate that the proposed model surpasses current state-of-the-art methods in performance while maintaining low computational costs, confirming its efficacy. This approach enhances the robot's ability to perceive complex environments, providing valuable insights for implementing firefighting actions in real-world scenarios.