Exploring 2D projection and 3D spatial information for aircraft 6D pose

The 6D pose estimation is important for the safe take-off and landing of the aircraft using a single RGB image. Due to the large scene and large depth, the exiting pose estimation methods have unstratified performance on the accuracy. To achieve precise 6D pose estimation of the aircraft, an end-to-end method using an RGB image is proposed. In the proposed method, the 2D and 3D information of the keypoints of the aircraft is used as the intermediate supervision, and 6D pose information of the aircraft in this intermediate information will be explored. Specifically, an off-the-shelf object detector is utilized to detect the Region of the Interest (RoI) of the aircraft to eliminate background distractions. The 2D projection and 3D spatial information of the pre-designed keypoints of the aircraft is predicted by the keypoint coordinate estimator (KpNet). The proposed method is trained in an end-to-end fashion. In addition, to deal with the lack of the related datasets, this paper builds the Aircraft 6D Pose dataset to train and test, which captures the take-off and landing process of three types of aircraft from 11 views. Compared with the latest Wide-Depth-Range method on this dataset, our proposed method improves the average 3D distance of model points metric (ADD) and 5° and 5 m metric by 86.8% and 30.1%, respectively. Furthermore, the proposed method gets 9.30 ms, 61.0% faster than YOLO6D with 23.86 ms.     

全球航空旅客周转量预测及运力分配方法

民机制造商在新飞机可行性论证、公司产品战略制定以及飞机销售中，需要了解航空市场规模和未来航空市场飞机的需求，为可行性论证提供投资规模计算输入，为制造商制定产品战略发展提供依据，为飞机销售计划提供数据支持。预测未来客机市场的需求量，其关键是对航空旅客周转量的预测。采用线性回归预测出航空旅客周转量，除以上座率得到航空市场运力需求，即可供座位数，并将运力需求分配后与各地区机队预测的运力供给相匹配，最终得出所需的飞机需求量。