Compressed Image Object Detection Using Snapshot Compressive Imaging
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Solution Overview
Problem
Traditional object detection methods in images and video are resource-intensive and slow due to a sequential workflow of capture, compression, reconstruction, and detection, which limits efficiency and accuracy, especially in dynamic scenes.
Innovation Solution
A system and method that performs object detection directly on compressed images using snapshot compressive imaging (SCI) and knowledge distillation, leveraging a pre-trained YOLO model with combined feature and task loss to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential workflow (capture, compression, reconstruction, detection) is used, then detection accuracy can be maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent combines compression and detection operations into a single integrated process. The neural network performs both compression of the input signal and detection of objects simultaneously, eliminating the need for separate reconstruction and detection steps. This merging of operations reduces processing time while maintaining detection accuracy through joint optimization of both functions.
Solution Approach 2:
The system performs compression as a preliminary action before detection, but in an integrated manner. The compression operation is embedded within the detection pipeline, allowing the system to prepare compressed representations that are immediately suitable for detection without requiring full reconstruction of the original signal. This preliminary compression action reduces the data volume that needs to be processed for detection.
2Reliability
If traditional sequential workflow is used, then comprehensive object information can be obtained, but computational resources and storage requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for detection from the compressed signal, rather than performing full reconstruction. The neural network identifies and processes relevant object information directly from the compressed representation, discarding redundant data. This extraction approach maintains detection reliability while significantly reducing computational resource requirements compared to processing full-resolution reconstructed images.
3Loss of substance
If compression is applied before detection, then data storage and transmission efficiency improve, but detection complexity increases
Solution Approach 1:
The patent changes the parameter space by operating directly in the compressed domain rather than requiring transformation back to the original image space. The neural network is trained to recognize objects in the compressed signal representation, effectively changing the detection parameters from pixel-based to compression-coefficient-based. This approach reduces data storage requirements while managing detection complexity through specialized network architecture designed for compressed input.
Data Source
AI summary
A system for detecting an object from an image includes: a computing apparatus having a processing unit, a memory unit and a user interface, the processing unit operatively coupled to the memory unit, the computing apparatus configured to: compress optical signals (i.e., visual signals) from a real world scene using a Snapshot Compressive Imaging (SCI) system to obtain compressed signals, receive the compressed signals, store the compressed signals as compressed images, apply one or more knowledge distillation techniques in conjunction with a pre trained object detection model to detect one or more objects directly from each compressed image, utilize motion information encoded within the compressed data to optimize the object detection process, and present on the user interface the one or more detected objects on an image.