基于RGB亮度分级的图像预处理研究

利用一种基于RGB亮度分级的图像预处理方法,在源图像的R、G、B三个分量上对图像进行亮度分级合并,以减少自然场景图像的颜色数目,在不破坏源图像结构的前提下降低图像的复杂度,同时又保证了较好的通用性。     

空间目标可见光相机的成像算法研究与分析

可见光相机可被应用于空间目标监测，其拍摄的图像具有高分辨率、高清晰度和高质量等优点。基于可见光探测器CMV4000获取Bayer格式数据的特点，采用了颜色插值和白平衡等方法，实现了彩色图像的重建。通过对比和分析多种插值方法，选取基于梯度的插值算法和灰度世界法用于工程实现色彩数据的重建。用彩色板等目标在实验室、暗室等场景下对算法进行仿真测试。结果表明：基于梯度的插值方法和白平衡法可以有效地恢复彩色图像，可以在空间环境中获取高质量的彩色图像。     

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.