Neural Network Object Detection Distance Estimation
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Solution Overview
Problem
Conventional object detection technologies using monocular cameras struggle to accurately estimate the distance to objects when assumptions do not apply, leading to incorrect distance calculations.
Innovation Solution
An object detecting apparatus and method that utilizes a neural network to simultaneously estimate the posture and distance of objects in a captured image, incorporating non-linear processing and a detecting unit to identify object-candidate regions and output object information, including distance, using a neural network trained for both tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional object detection technologies use monocular cameras with assumptions to estimate distance, then the detection process is simple, but the distance estimation accuracy deteriorates when assumptions do not apply
Solution Approach 1:
The patent combines object detection and distance estimation into a unified neural network model that processes the captured image simultaneously for both tasks. The detecting unit identifies object-candidate regions while the non-linear processing unit estimates distance using the same image data, merging two previously separate functions into one integrated system that improves distance accuracy without proportionally increasing complexity
Solution Approach 2:
The neural network is designed to perform multiple functions: detecting objects, estimating their posture, and calculating distance simultaneously. This multi-functional approach allows the system to leverage shared features and computations across tasks, improving distance estimation accuracy while maintaining reasonable system complexity through efficient resource utilization
2Device complexity
If conventional technologies detect objects and estimate distance separately, then the detection algorithm is simple, but the overall detection accuracy deteriorates
Solution Approach 1:
The patent merges object detection and distance estimation into a single integrated neural network processing pipeline. The detecting unit and non-linear processing unit work together to simultaneously identify objects and calculate their distance from the captured image, ensuring that both tasks benefit from shared feature extraction and coordinated optimization, thereby improving overall detection reliability
Solution Approach 2:
The system employs feedback mechanisms where the output of the detecting unit informs the non-linear processing unit, and the distance estimation results can be used to refine object detection. This iterative feedback loop between detection and distance estimation allows the system to continuously improve accuracy by leveraging information from both tasks
Data Source
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AI summary
According to an arrangement, an object detecting apparatus (1) includes a detecting unit (10; 12) and a non-linear processing unit (10; 13). The detecting unit (10; 12) is configured to detect one or more object-candidate regions from a captured image. The non-linear processing unit (10; 13) is configured to input the entire captured image or a part of the captured image at least including the object-candidate region to a neural network having been trained to estimate the posture of an object in the object-candidate region and the distance to the object simultaneously, and output object information at least including information on the distance to the object, using an output from the neural network.