Multi-Agent Autonomous Collaborative Detection Method for Multi-Targets in Complex Fire Environments

When a fire breaks out in a high-rise building, the occlusion of smoke and obstacles results in dearth of crucial information concerning people in distress, thereby creating a challenge in their detection. Given the restricted sensing range of a single unmanned aerial vehicle (UAV) camera, enhancing the target recognition rate becomes challenging without target information. To tackle this issue, this paper proposes a multi-agent autonomous collaborative detection method for multi-targets in complex fire environments. The objective is to achieve the fusion of multi-angle visual information, effectively increasing the target’s information dimension, and ultimately addressing the problem of low target recognition rate caused by the lack of target information. The method steps are as follows: first, the you only look once version5 (YOLOv5) is used to detect the target in the image; second, the detected targets are tracked to monitor their movements and trajectories; third, the person re-identification (ReID) model is employed to extract the appearance features of targets; finally, by fusing the visual information from multi-angle cameras, the method achieves multi-agent autonomous collaborative detection. The experimental results show that the method effectively combines the visual information from multi-angle cameras, resulting in improved detection efficiency for people in distress.