Monocular Depth Estimation Using Neural Networks
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Solution Overview
Problem
Existing depth estimation methods face challenges in accuracy and cost, with high-accuracy methods using large and costly hardware devices like LiDAR, and low-cost vision sensors providing less accurate results, especially for targets far away, while monocular image-based methods are dependent on training data and computational resources.
Innovation Solution
An image processing method and apparatus that estimates depth using single-task and multi-task learning networks, preprocessing images based on focal length information, and employing region-based convolutional neural networks and You Only Look Once (YOLO2) frameworks to determine target depth through position, size, and class analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR or other hardware devices are used for depth estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical depth sensing systems (LiDAR, stereo cameras) with a monocular vision system that uses deep learning neural networks to estimate depth. The system substitutes physical depth measurement mechanisms with computational algorithms that process single-image data to infer depth information, thereby reducing hardware complexity while maintaining reasonable accuracy
Solution Approach 2:
The patent transforms the depth estimation problem from a direct physical measurement task into a parameter-based computational task. By using neural networks to learn relationships between image features and depth parameters, the system changes the approach from mechanical measurement to parameter inference, reducing hardware requirements
2Device complexity
If monocular image-based deep learning methods are used, then device complexity is reduced, but measurement precision and computational resource requirements become problematic
Solution Approach 1:
The patent segments the depth estimation task into multiple processing stages: image preprocessing, neural network feature extraction, depth parameter prediction, and post-processing refinement. This segmentation allows the system to achieve higher accuracy through systematic processing while maintaining the simplicity of monocular input
Solution Approach 2:
The patent performs preliminary preprocessing on the input image before depth estimation, including normalization and feature enhancement operations. This preliminary action prepares the data to maximize the neural network's depth prediction accuracy, addressing the precision challenge before the main estimation task
3Measurement precision
If two vision sensors are used for depth estimation, then measurement precision is improved compared to single sensor, but device complexity increases
Solution Approach 1:
The patent creates a virtual copy of the depth information through neural network prediction. Instead of using a second physical sensor, the system uses deep learning to generate predicted depth maps that replicate the information a second sensor would provide, thereby reducing hardware complexity while maintaining accuracy
Data Source
AI summary
An image processing method and apparatus is disclosed. The image processing method includes receiving an input image and estimating a depth of a target based on a position, a size, and a class of the target in the input image.