RGB-D cameras have been successfully used for indoor High-ThroughpuT Phenotyping (HTTP). However, their capability and feasibility for in-field HTTP still need to be evaluated, due to the noise and disturbances generated by unstable illumination, specular reflection, and diffuse reflection, etc. To solve these problems, we evaluated the depth-ranging performances of two consumer-level RGB-D cameras (RealSense D435i and Kinect V2) under in-field HTTP scenarios, and proposed a strategy to compensate the depth measurement error. For performance evaluation, we focused on determining their optimal ranging areas for different crop organs. Based on the evaluation results, we proposed a brightness-and-distance-based Support Vector Regression Strategy, to compensate the ranging error. Furthermore, we analyzed the depth filling rate of two RGB-D cameras under different lighting intensities. Experimental results showed that: 1) For RealSense D435i, its effective ranging area is [0.160, 1.400] m, and in-field filling rate is approximately 90%. 2) For Kinect V2, it has a high ranging accuracy in the [0.497, 1.200] m, but its in-field filling rate is less than 24.9%. 3) Our error compensation model can effectively reduce the influences of lighting intensity and target distance. The maximum MSE and minimum R2 of this model are 0.029 and 0.867, respectively. To sum up, RealSense D435i has better ranging performances than Kinect V2 on in-field HTTP.
翻译:RGB-D照相机已成功地用于室内高槽图谱制作(HTTP),但是,由于不稳定的照明、镜像反射和扩散反射等造成的噪音和扰动,仍需要评估其在现场的HTTP的能力和可行性。为了解决这些问题,我们评估了两个消费者级RGB-D照相机(RealSense D435i和Kinect V2)在现场HTTP情景下的深度性能,并提出了弥补深度测量误差的战略。在绩效评估方面,我们侧重于确定不同作物器官的最佳范围。根据评估结果,我们提出了基于亮度和距离的支持矢量反射战略,以弥补范围错误。此外,我们分析了两个消费者级RGB-D照相机(RealSense D435i和Kinect V35i)在现场的深度性能,其有效范围为[0.160、1.400] m,在现场填充率率约为90.S最短的距离值支持矢量值战略。