콘텐츠 본문
논문 국내 국제전문학술지(SCI급) A Multi-output Convolutional Neural Network-based Distance and Velocity Estimation Technique
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2022-03
- 저자명 정의림공동(교신),최재웅
- 학술지명 Journal of Logistics, Informatics and Service Science
- 발행처명 Success Culture Press
- 발행국가 국내
- 논문언어 한국어
- 전체저자수 2
논문 초록 (Abstract)
Distance and depth detection plays a crucial role in intelligent robotics. It enables drones to understand their working environment to avoid collisions and accidents immediately and is very important in various AI applications. Image-based distance detection usually relies on the correctness of geometric information. However, the geometric features will be lost when the object is rotated or the camera lens image is distorted. This study proposes a training model based on a convolutional neural network, which uses a single-lens camera to estimate humans’ distance in continuous images. We can partially restore depth information loss using built-in camera parameters that do not require additional correction. The normalized skeleton feature unit vector has the same characteristics as time series data and can be classified very well using a 1D convolutional neural network. According to our results, the accuracy for the occluded leg image is over 90% at 2 to 3 m, 80% to 90% at 4 m, and 70% at 5 to 6 m.
