Mei Zhang

Light Detection and Ranging (LiDAR) is widely used in the perception of physical environment to complete object detection and tracking tasks. The current methods and datasets are mainly developed for autonomous vehicles, which could not be directly used for roadside perception. This paper presents a 3D point cloud stitching method for object detection with wide horizontal field of view (FoV) using roadside LiDAR. Firstly, the base detection model is trained by KITTI dataset and has achieved detection accuracy of 88.94. Then, a new detection range of 180° can be inferred to break the limitation of camera’s FoV. Finally, multiple sets of detection results from a single LiDAR are stitched to build a 360° detection range and solve the problem of overlapping objects. The effectiveness of the proposed approach has been evaluated using KITTI dataset and collected point clouds. The experimental results show that the point cloud stitching method offers a cost-effective solution to achieve a larger FoV, and the number of output objects has increased by 77.15% more than the base model, which improves the detection performance of roadside LiDAR.

The application of infrared camera-related technology is a trending research topic. By reviewing the development of infrared thermal imagers, this paper introduces several main processing technologies of infrared thermal imagers, expounds the image nonuniformity correction, noise removal, and image pseudo color enhancement of infrared thermal imagers, and briefly analyzes some main algorithms used in image processing. The technologies of blind element detection and compensation, temperature measurement, target detection, and tracking of infrared thermal imager are described. By analyzing the main algorithms of infrared temperature measurement, target detection, and tracking, the advantages and disadvantages of these technologies are put forward. At the same time, the development of multi/hyperspectral infrared remote sensing technology and its application are also introduced. The analysis shows that infrared thermal imager processing technology is widely used in many fields, especially in the direction of autonomous driving, and this review helps to expand the reader’s research ideas and research methods.