Tiny Object Detection via Color and Motion Signal Stacking
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Solution Overview
Problem
Accurate detection of unmanned aerial vehicles (UAVs) using computer vision is challenging, especially when captured at a distance due to low pixel representation, motion blur, occlusion, and digital noise. Existing methods struggle to achieve a balance between computational accuracy and robust detection across diverse conditions.
Innovation Solution
The system employs an object detection system that processes image data from multiple frames, combining color data and motion data to detect targeted objects represented by a small number of pixels. This is achieved through a network model that includes a modified YOLO architecture, utilizing Gaussian Receptive Fields and processing image tiles to capture spatial and temporal features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision methods are used to detect objects at a distance, then the system is simple to implement, but the detection accuracy deteriorates due to low pixel representation
Solution Approach 1:
The patent combines color information from multiple image frames with motion information derived from optical flow calculations, merging these different data types into a unified detection framework. This integration allows the system to leverage both spatial color features and temporal motion patterns, significantly improving detection accuracy for small distant objects while managing system complexity through efficient data fusion strategies
2Measurement precision
If dense flow calculations are used to utilize motion data, then motion detection accuracy improves, but computational complexity becomes too high for real-time implementation
Solution Approach 1:
The patent implements optical flow calculations selectively rather than computing dense flow fields across the entire image. By calculating motion data only in regions where small objects are detected or where motion is most likely to occur, the system achieves sufficient motion detection accuracy while maintaining real-time processing capabilities. This partial action approach avoids the computational burden of complete dense flow calculations
3Measurement precision
If the system processes more image data to improve detection of tiny objects, then detection accuracy improves, but computational time increases
Solution Approach 1:
The patent performs preliminary processing of image frames by extracting color features and computing optical flow data before the main detection algorithm is applied. This preliminary action prepares the data in advance, allowing the detection network to work with pre-processed information that highlights relevant features. By doing this preparation work beforehand, the system improves tiny object detection accuracy while reducing the computational time required during the actual detection phase
Data Source
AI summary
A system can include an image capture system configured to obtain image data comprising at least a first image frame and a second image frame and a memory storing instructions that, when executed by one or more processors, cause the one or more processors to process a first set of image data based on the first image frame, process a second set of image data based on the first image frame and the second image frame, and execute a network model configured to detect one or more targeted objects from a plurality of potential objects in the first image frame based on an input comprising the first set of image data and the second set of image data, the one or more targeted objects comprising less than 1/100th pixels of a total number of pixels in the first image frame.