低光照下的无人机异物检测与定位

为解决无人机在低光照环境下的巡检过程中, 不能对场景中的异物进行识别与定位, 导致后续智能算法无法获得环境语义信息的问题. 本文提出一种将ORB-SLAM2算法与适用于低光照目标检测改进的YOLOv5模型进行信息融合的方法. 首先, 通过RGB-D相机自采集低光照数据集进行深度学习训练及融合算法验证. 然后, 结合关键帧信息、目标检测模块的输出结果以及相机的固有信息完成目标像素坐标提取. 最后, 通过关键帧信息和像素坐标完成目标物体相对世界坐标系的位置解算. 本文实现了低光照环境下目标物体较为准确的识别和目标物体在世界坐标系中分米级的定位, 为低光照环境下无人机智能巡检提供了一种有效的解决方案.

Abnormal Object Detection and Localization of Unmanned Aerial Vehicles in Low-light Environment

Unmanned aerial vehicles (UAVs) cannot identify and locate foreign objects in the scene during the inspection in low-light environments, resulting in the subsequent intelligent algorithms failing to obtain the environmental semantic information. To this end, this study proposes a method to fuse information from the ORB-SLAM2 algorithm with the YOLOv5 model, which is applicable to the improvement of low-light object detection. First, deep learning training and fusion algorithm validation are performed by self-collecting low-light datasets from RGB-D cameras. Then, the target pixel coordinates are extracted by combining the keyframe information, the output of the object detection module, and the inherent information of the camera. Finally, the position of the target object is solved relative to the world coordinate system by keyframe information and pixel coordinates. The study achieves more accurate recognition of target objects in low-light environments and localization of target objects in the world coordinate system at the sub-meter level, which provides an effective solution for intelligent inspection of UAVs in low-light environments.