DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds

Abstract

Three-dimensional virtual environments can be configured as test environments of autonomous things, and remote sensing by 3D point clouds collected by light detection and range (LiDAR) can be used to detect virtual human objects by segmenting collected 3D point clouds in a virtual environment. The use of a traditional encoder-decoder model, such as DeepLabV3, improves the quality of the low-density 3D point clouds of human objects, where the quality is determined by the measurement gap of the LiDAR lasers. However, whenever a human object with a surrounding environment in a 3D point cloud is used by the traditional encoder-decoder model, it is difficult to increase the density fitting of the human object. This paper proposes a DeepLabV3-Refiner model, which is a model that refines the fit of human objects using human objects whose density has been increased through DeepLabV3. An RGB image that has a segmented human object is defined as a dense segmented image. DeepLabV3 is used to make predictions of dense segmented images and 3D point clouds for human objects in 3D point clouds. In the Refiner model, the results of DeepLabV3 are refined to fit human objects, and a dense segmented image fit to human objects is predicted. The dense 3D point cloud is calculated using the dense segmented image provided by the DeepLabV3-Refiner model. The 3D point clouds that were analyzed by the DeepLabV3-Refiner model had a 4-fold increase in density, which was verified experimentally. The proposed method had a 0.6% increase in density accuracy compared to that of DeepLabV3, and a 2.8-fold increase in the density corresponding to the human object. The proposed method was able to provide a 3D point cloud that increased the density to fit the human object. The proposed method can be used to provide an accurate 3D virtual environment by using the improved 3D point clouds.

CHERRY SEGMENTATION AND IDENTIFICATION BASED ON DeepLabV3 IN COMPLEX ORCHARD ENVIRONMENT

“…On the basis of DeepLabV1, DeepLabV2 can achieve better performance by using multi-scale processing and ASPP. DeepLabV3 expands ASPP module to better solve segmentation problems at multiple scales (Kwak and Sung, 2021). Among all the methods, the identification results of our method are the highest, the MIoU is 91.06%, the MPA is 93.05%, and kappa coefficient is 0.89, and the overall classification effect is "Good".…”

“…Producer accuracy (PA) represents the probability that a certain type of sample in the classification diagram is correctly classified, as shown in formula (7):…”

An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry

“…Introduced by Google researchers in 2018 [29], DeepLabv3+ is a state-of-the-art CNN model specifically designed for semantic segmentation tasks and represents the latest upgraded version of the DeepLab series models. It has achieved remarkable performances and found wide application in the domain of semantic segmentation [30][31][32][33]. As depicted in Figure 3, DeepLabv3+ comprises two primary components, an encoder and decoder [34].…”

Extraction of typical urban buildings in China based on K-Net

“…The current mainstream deep learning segmentation networks are usually based on the deeplab family. DeepLabv3 5 and ResUNeXt++ 6 networks are proposed. DeepLabv3 improves the ASPP module, which can better obtain global information; ResUNeXt++ adopts a method of cascading multi-scale feature fusion to fuse semantic information multiple times.…”