Laser stripe segmentation and centerline extraction based on 3D scanning imaging

LaserNet: a method of laser stripe center extraction under non-ideal conditions

The extraction of the center of a laser stripe is a key step in line-structure measurement, where noise interference and changes in the surface color of an object are the main factors affecting extraction accuracy. To obtain sub-pixel level center coordinates under such non-ideal conditions, we propose LaserNet, a novel deep learning-based algorithm, to the best of our knowledge, which consists of a laser region detection sub-network and a laser position optimization sub-network. The laser region detection sub-network is used to determine potential stripe regions, and the laser position optimization sub-network uses the local image of these regions to obtain the accurate center position of the laser stripe. The experimental results show that LaserNet can eliminate noise interference, handle color changes, and give accurate results under non-ideal conditions. The three-dimensional reconstruction experiments further demonstrate the effectiveness of the proposed method.

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting

Visual sorting of express packages is faced with many problems such as the various types, complex status, and the changeable detection environment, resulting in low sorting efficiency. In order to improve the sorting efficiency of packages under complex logistics sorting, a multi-dimensional fusion method (MDFM) for visual sorting in actual complex scenes is proposed. In MDFM, the Mask R-CNN is designed and applied to detect and recognize different kinds of express packages in complex scenes. Combined with the boundary information of 2D instance segmentation from Mask R-CNN, the 3D point cloud data of grasping surface is accurately filtered and fitted to determining the optimal grasping position and sorting vector. The images of box, bag, and envelope, which are the most common types of express packages in logistics transportation, are collected and the dataset is made. The experiments with Mask R-CNN and robot sorting were carried out. The results show that Mask R-CNN achieves better results in object detection and instance segmentation on the express packages, and the robot sorting success rate by the MDFM reaches 97.2%, improving 2.9, 7.5, and 8.0 percentage points, respectively, compared to baseline methods. The MDFM is suitable for complex and diverse actual logistics sorting scenes, and improves the efficiency of logistics sorting, which has great application value.