Probabilistic Object Detection via Neural Process Latent Space
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Solution Overview
Problem
Current object detection systems in vehicles are deterministic, providing fixed bounding box results without probabilistic information, which is essential for downstream applications like autonomous vehicle control.
Innovation Solution
An object detection system that generates a probability distribution of the bounding box location using a neural process, incorporating context points, a latent space, and a centered pixel location, enabling the estimation of object presence and position uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a deterministic object detection system is used to generate fixed bounding box results, then the system is simple and fast to operate, but it lacks probabilistic information required for downstream applications
Solution Approach 1:
The patent transforms the detection output from fixed deterministic values to probability distributions by changing the parameter representation. Instead of outputting single bounding box coordinates, the system outputs probabilistic distributions over possible locations, enabling downstream applications to quantify uncertainty while maintaining computational tractability through parameterized distributions
2Reliability
If a deterministic object detection system generates fixed bounding box results, then the processing speed is high, but the reliability for autonomous vehicle control is insufficient
Solution Approach 1:
The patent extracts only the essential probabilistic information needed for reliability assessment from the full detection process. By using parameterized probability distributions rather than complete uncertainty models, the system captures the necessary reliability data while avoiding the computational burden of full probabilistic propagation through the entire detection pipeline
3Adaptability or versatility
If probabilistic information is incorporated into object detection results, then downstream applications can make better decisions, but the computational complexity increases
Solution Approach 1:
The patent changes the output parameters from fixed values to probability distributions, which provides adaptability to downstream applications that can incorporate uncertainty in their decision-making. The parameterized nature of these distributions allows efficient computation and integration with various downstream tasks without requiring complex custom implementations for each application
Solution Approach 2:
The probabilistic output format serves multiple downstream applications universally. The same probability distribution output can be used for risk assessment, path planning, object tracking, and other autonomous vehicle functions, making the detection system versatile without requiring application-specific customization of the detection algorithm
Data Source
AI summary
An object detection system for generating a probability distribution of the location of an object includes one or more processors and a memory in communication with the one or more processors. The memory includes an image acquisition module, a bounding box generator module, and a probability distribution generator module. The image acquisition module causes the one or more processors to obtain a two-dimension image displaying an object. The bounding box generator module causes the one or more processors to generate, using the two-dimensional image as an input, a bounding box of the object displayed in the two-dimensional image having a plurality of pixels. The probability distribution generator module causes the one or more processors to generate a probability distribution of a bounding box location for the object using a neural process using a pair of context points, a latent space, and a centered pixel location.