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Impact of ISP Tuning on Object Detection.

Dara Molloy1,2, Brian Deegan1,2, Darragh Mullins1,2

  • 1School of Engineering, University of Galway, University Road, H91 TK33 Galway, Ireland.

Journal of Imaging
|December 22, 2023
PubMed
Summary
This summary is machine-generated.

Altering image signal processor (ISP) settings in cameras degrades object detection performance for advanced driver assistance systems (ADAS). This study reveals varying ISP parameters impacts model sensitivity, proposing data augmentation to enhance robustness.

Keywords:
ADASautonomous vehiclesimage signal processingobject detection

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Area of Science:

  • Computer Vision
  • Autonomous Systems
  • Machine Learning

Background:

  • Camera-based object detection is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles.
  • Image signal processors (ISPs) are tuned for human perception, with subjective parameter selection impacting image quality.
  • The performance implications of ISP parameter variations on deep learning object detection models remain unclear.

Purpose of the Study:

  • To analyze the impact of image signal processor (ISP) parameter variations on object detection models used in ADAS.
  • To investigate the sensitivity of popular object detection models to changes in eight key ISP blocks.
  • To propose a method for improving object detection model robustness against ISP variations.

Main Methods:

  • Evaluated 14 object detection models across PASCALRAW and a custom ADAS raw dataset.
  • Systematically varied parameters in eight ISP blocks: demosaicing, gamma, denoising, edge enhancement, local tone mapping, saturation, contrast, and hue angle.
  • Developed a novel data augmentation methodology based on ISP parameter variations.

Main Results:

  • Deviations from default ISP settings generally degrade object detection performance.
  • Different object detection models exhibit varying degrees of sensitivity to ISP parameter changes.
  • The proposed ISP variation data augmentation technique enhances model robustness.

Conclusions:

  • ISP parameter tuning significantly affects camera-based object detection for ADAS.
  • Understanding model sensitivity to ISP variations is critical for reliable autonomous systems.
  • Data augmentation using ISP variations offers a promising approach to improve the robustness of object detection models.