Autonomous driving paper index
Distance Estimation for Antenna Recognition Using AI and a Monocular RGB Camera
One-line summary
Object detection is one of the main applications of computer vision based on artificial intelligence (AI), with broad benefits in various fields such as safety monitoring, autonomous vehicles, smart agriculture, and underwater environmental observation.
Engineering notes
Key topics: autonomous driving, autonomous vehicle, object detection. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Object detection is one of the main applications of computer vision based on artificial intelligence (AI), with broad benefits in various fields such as safety monitoring, autonomous vehicles, smart agriculture, and underwater environmental observation. One of the further developments of object detection is the estimation of the distance of objects from the camera, which generally relies on depth cameras. Although accurate, depth cameras are relatively expensive, making them less suitable for home or small-scale industrial applications. This study aims to evaluate the potential use of monocular RGB cameras as an alternative to depth cameras in detecting object distances. With a more affordable cost, monocular RGB cameras are expected to be able to provide fairly accurate distance estimates for certain needs. This paper discusses the approach used, as well as the potential and limitations of RGB camera-based distance estimation methods. Our study found that the system can detect dipole and microstrip antenna types, with the average error being 10.94 cm and 37.07 cm, respectively.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments