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Compression-Efficient Feature Extraction Method for a CMOS Image Sensor.

Keiichiro Kuroda1, Yu Osuka1, Ryoya Iegaki1

  • 1Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-shi 525-8577, Japan.

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Summary

This study introduces a new method for extracting binary image features for the Internet of Things (IoT), drastically cutting data size by 99% while preserving recognition accuracy. This enables efficient, low-power image recognition systems for edge devices.

Keywords:
CMOS image sensordata reductionfeature extractionobject recognitionrun-length encoding

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Area of Science:

  • Computer Vision
  • Image Processing
  • Embedded Systems

Background:

  • Power constraints in the Internet of Things (IoT) era necessitate efficient data handling.
  • Conventional image sensors generate large data volumes, challenging low-power applications.
  • Existing methods often involve a trade-off between data size and recognition accuracy.

Purpose of the Study:

  • To develop a compression-efficient feature extraction method for CMOS image sensors.
  • To enable low-power image recognition systems for the IoT era.
  • To reduce data transmission volume without compromising image recognition accuracy.

Main Methods:

  • Proposed a novel feature extraction method for CMOS image sensors.
  • Extracted six-channel binary feature data (luminance and horizontal edge signals).
  • Employed run-length encoding (RLE) for data compression.

Main Results:

  • Achieved 60.7% APL50 object recognition accuracy on the COCO dataset using YOLOv7 on edge GPUs, with a 99.2% data size reduction.
  • Attained 89.4% image classification accuracy on the Visual Wake Words (VWW) dataset using MobileNetV3, with a 99.0% data size reduction.
  • Demonstrated superior performance over conventional methods by reducing data size significantly while maintaining high accuracy.

Conclusions:

  • The proposed method effectively reduces data size for image recognition in power-constrained IoT environments.
  • This approach overcomes the traditional accuracy-data size trade-off.
  • Enables the practical realization of efficient, low-power image recognition systems.