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Structured deep learning based object-specific distance estimation from a monocular image
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DOI：10.1007/s13042-023-01887-6.png)
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Abstract
En 中文
Distance calculation is a critical link in the research fields of object trajectory prediction, automatic driving obstacle avoidance, and so on. However, the research on distance using deep learning methods has yet to attract wide attention. The accuracy of traditional distance estimation algorithms based on the optical principle and mathematical modeling is low in practical applications, mainly the curve or slope of the road surface. This paper addresses the challenging distance estimation problem by developing an end-to-end structured model to directly predict the distance for objects in a given image. Besides, the traditional mathematical modeling process is replaced by this learning-based method. To facilitate the research on this task, we construct the extended distance datasets by KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) and NYU(Nathan Silberman, Pushmeet Kohli, Derek Hoiem, Rob Fergus) Depth V2 distance datasets. Experimental results demonstrate that the structured learning model has higher accuracy than the traditional algorithm in different distance ranges and better performance for curves and ramps. Moreover, improving neural network performance will be the direction of improving the model in the future.
Keywords:
Distance estimation
Convolutional neural network
Monocular image
Structured deep learning
Conditional random field
Machine learning
Computer vision
Single camera
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