Recent advances in 3D object detection based on RGB-D: A survey

3D object detection is a critical part of environmental perception systems and one of the most fundamental tasks in understanding the 3D visual world, which benefit a series of downstream real-world applications. RGB-D images include object texture and semantic information, as well as depth information describing spatial geometry. Recently, numerous 3D object detection models for RGB-D images have been proposed with excellent performance, but summaries in this area are still absent. To stimulate future research, this paper provides a detailed analysis of current developments in 3D object detection methods for RGB-D images to motivate future research. It covers three major parts, including background on 3D object detection, RGB-D data details, and comparative results of state-of-the-art methods on several publicly available datasets, with an emphasis on contributions, design ideas, and limitations, as well as insightful observations and inspiring future research directions.     

基于比色法和光谱法的土壤中磷的快速检测 方法研究

目的研发土壤中磷素的快速检测方法。方法针对土壤中磷含量的快速检测,利用比色法和光谱法构建了磷钼蓝反应的吸收曲线模型,并对一种智能土肥养份综合测试仪进行了测试。结果在检测波长为870nm时,将2.0 m L的26 g/L的钼酸铵溶液、1.0 m L的100 g/L抗坏血酸溶液和0.4 g/L的EDTA添加到酸性反应体系中,通过使用紫外-可见分光光度处理所测得的数据,可知磷溶液浓度与吸光度之间呈线性关系,得到线性回归模型为Y=0.01558X-0.1106,决定系数(r~2)=0.995。对这种智能土肥养份综合测试仪进行准确性测试,线性偏差在3%内,通过与所构建的线性回归模型的计算值进行对比,预测值误差在4%以内。结论所构建的线性回归模型是可用的,这种智能土肥养份综合测试仪可满足土壤中磷的快速检测要求。