3D profilometric object detection in turbid water using integral imaging and deep neural networks

We evaluate a three-dimensional (3D) object detection system for operation in turbid water based on 3D profilometric Integral Imaging (InIm) and a deep neural network. While conventional InIm computational reconstruction provides the two-dimensional (2D) slices of the 3D scene at specific 2D depth planes, 3D profilometry allows visualization of the 3D surface from unique perspectives. In the proposed method, we develop a deep neural network-based red, green, blue-depth (RGB-D) object detection framework using passive 3D profilometry under turbid conditions. An image sensor on a moving platform captures multiple 2D perspective images of the 3D scene, from which a depth map is statistically estimated. The captured perspective image and the estimated depth map are then fused to generate a four-channel RGB-D image for 3D object detection in turbidity. Comparative experiments are conducted and demonstrate that the proposed 3D profilometry-based approach outperforms both 2D imaging and conventional 3D InIm-based reconstruction across various turbidity levels evaluated. To the best of our knowledge, this is the first report on InIm 3D profilometry for object detection in turbid water.