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Related Experiment Video

Updated: Jan 25, 2026

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Low-Cost Image Compressive Sensing with Multiple Measurement Rates for Object Detection.

Longlong Liao1, Kenli Li2, Canqun Yang3

  • 1College of Computer, National University of Defense Technology, Changsha 410073, China. liaolonglong16@nudt.edu.cn.

Sensors (Basel, Switzerland)
|May 8, 2019
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Summary

This study introduces MRCS, a low-cost compressive sensing (CS) method for object detection. MRCS reduces data transmission and storage by using multiple measurement rates and half-precision for CS measurements, improving efficiency in surveillance systems.

Keywords:
compression ratiocompressive sensinghalf-precision floatmultiple measurement ratesobject detection

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Machine Learning

Background:

  • Compressive Sensing (CS) methods face challenges with transmission/storage overheads at high measurement rates and degraded reconstruction quality at low rates.
  • Large-scale distributed surveillance systems require efficient data handling for object detection.

Purpose of the Study:

  • To propose a low-cost image CS approach (MRCS) for object detection that addresses the limitations of existing CS methods.
  • To reduce the overheads of transmission and storage for CS measurements in resource-limited scenarios.

Main Methods:

  • Developed MYOLO3, a smaller and improved object detector for resource-constrained hardware.
  • Implemented MRCS which samples key object regions and other areas using multiple measurement rates.
  • Utilized half-precision CS measurements for storage and transmission to further reduce data size.

Main Results:

  • MYOLO3 demonstrates improved performance on resource-limited devices like surveillance cameras and drones.
  • MRCS significantly reduces transmission bandwidth and storage space, achieving mean Compression Ratios (mCR) of 1.43-22.92 on the VOC-pbc dataset.
  • Half-precision representations in MRCS halve the required bandwidth and storage compared to single-precision counterparts.

Conclusions:

  • MRCS effectively reduces CS measurement size through multiple rates and half-precision, enhancing object detection usability.
  • The proposed approach offers a practical solution for efficient object detection in large-scale distributed surveillance systems.