Image Interpolation for Multi-Sensor Feature Detection Training
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Solution Overview
Problem
Existing feature detection systems face challenges in accurately training feature detection models when sensor capture timings are not aligned, particularly in multi-sensor environments where camera and DAR sensor frames are captured at different times.
Innovation Solution
The method involves obtaining a DAR frame and corresponding image frames from a camera, interpolating the image frames to create a time-aligned interpolated image frame using a generative machine-learning model, and then training a feature detection model using the DAR frame and the interpolated image frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image frames and DAR frames are captured at different times by camera and DAR sensor, then multi-sensor data can be collected independently, but the training accuracy of feature detection model deteriorates due to temporal misalignment
Solution Approach 1:
The system performs preliminary actions by capturing multiple image frames at different times before the DAR frame capture, then uses these pre-captured frames to generate an interpolated image that matches the DAR frame's capture time. This preliminary capture of multiple frames enables temporal alignment without requiring simultaneous capture from both sensors.
Solution Approach 2:
The system introduces an intermediary element - the interpolated image generated by the machine learning model - that mediates between the camera's image frames and the DAR sensor's depth information. This interpolated image serves as a temporal bridge, allowing the feature detection model to be trained with aligned multi-sensor data even though the original captures occurred at different times.
2Measurement precision
If image frames are interpolated using machine learning model to align with DAR frame timing, then temporal alignment between sensors is improved, but computational complexity increases
Solution Approach 1:
The system employs self-service by using the camera's own previously captured image frames to generate the interpolated image. The machine learning model is trained on the camera's own data patterns, allowing it to predict what the image would have looked like at the DAR frame's capture time without requiring external reference data or complex multi-sensor synchronization hardware.
3Measurement precision
If multiple image frames are captured to enable interpolation, then temporal alignment capability is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary action by capturing multiple image frames in advance of the DAR frame capture. These frames are stored and later used for interpolation, allowing the actual processing to happen after the fact rather than requiring real-time computation during the critical capture moment.
Data Source
AI summary
Image interpolation techniques for multi-sensor training of feature detection models are disclosed. The techniques can include obtaining a detection-and-ranging (DAR) frame captured by a DAR sensor, obtaining a first image frame captured at a first time by a camera and a second image frame captured at a second time by the camera, wherein at a capture time of the DAR frame, a field of view (FOV) of the DAR sensor overlaps an FOV of the camera, and wherein the capture time of the DAR frame is between the first time and the second time, interpolating based on the first image frame and the second image frame to create an interpolated image frame, wherein a nominal capture time of the interpolated image frame corresponds to the capture time of the DAR frame, and training a feature detection model using the DAR frame and the interpolated image frame.