OPT-IQA: Automated camera parameters tuning framework with IQA-guided optimization

Abstract

In industrial visual inspection, computer vision-based AI systems play a pivotal role, with performances dependent on the quality of the acquired images and changes in environmental conditions. Modern cameras adapt to these varying environments by allowing the tuning of a wide range of camera parameters that significantly change the characteristics of the acquired images. While some parameters are already automatically adjusted in most cameras (e.g., exposure, focus, white balance), others are static and remain at their default values (e.g., brightness, contrast, color-saturation, sharpness). Adaptably adjusting these non-automatic (NAUTO) parameters significantly influences both image quality and the performance of automated visual inspection systems. This work introduces OPT-IQA, a novel framework to automate NAUTO parameter tuning. The proposed approach is based on an optimization process guided by Image Quality Assessment (IQA) metrics that measure human-understandable image quality characteristics, thus enhancing the interpretability of the parameters’ selection process. The framework is built modularly, including a Camera Abstraction Layer to ensure its camera-agnostic nature and a Region-of-Interest Selection Module to select the target region of the inspected object. It also facilitates the seamless integration of supplementary IQA metrics and optimization algorithms to support additional use cases. By using an IQA-guided optimization process based on a reference image, our results show that OPT-IQA alleviates the burden of manually adjusting NAUTO parameters in response to varying illumination conditions, whether caused by shifts in natural elements (e.g., weather) or human-induced changes (e.g., reconfiguration of assembly lines).