Enhancing the quality of gauge images captured in smoke and haze scenes through deep learning

TL;DR

This work tackles the challenge of reading analog gauges in smoke and haze where visibility is severely degraded. It adopts two deep-learning dehazing/desmoking networks, FFA-Net and AECR-Net, trained on a large synthetic gauge dataset generated in Unreal Engine with an 80/10/10 split. Results show AECR-Net consistently yields higher structural fidelity and perceptual quality than FFA-Net—up to about $SSIM \approx 0.98$ and $PSNR \approx 44$ dB on haze, with meaningful gains on smoke—and that both networks outperform the classical BCCR baseline. Importantly, enhanced gauge images enable an autonomous gauge-reading system to successfully detect and read gauges, illustrating practical impact for first responders and infrastructure monitoring.

Abstract

Images captured in hazy and smoky environments suffer from reduced visibility, posing a challenge when monitoring infrastructures and hindering emergency services during critical situations. The proposed work investigates the use of the deep learning models to enhance the automatic, machine-based readability of gauge in smoky environments, with accurate gauge data interpretation serving as a valuable tool for first responders. The study utilizes two deep learning architectures, FFA-Net and AECR-Net, to improve the visibility of gauge images, corrupted with light up to dense haze and smoke. Since benchmark datasets of analog gauge images are unavailable, a new synthetic dataset, containing over 14,000 images, was generated using the Unreal Engine. The models were trained with an 80\% train, 10\% validation, and 10\% test split for the haze and smoke dataset, respectively. For the synthetic haze dataset, the SSIM and PSNR metrics are about 0.98 and 43\,dB, respectively, comparing well to state-of-the art results. Additionally, more robust results are retrieved from the AECR-Net, when compared to the FFA-Net. Although the results from the synthetic smoke dataset are poorer, the trained models achieve interesting results. In general, imaging in the presence of smoke are more difficult to enhance given the inhomogeneity and high density. Secondly, FFA-Net and AECR-Net are implemented to dehaze and not to desmoke images. This work shows that use of deep learning architectures can improve the quality of analog gauge images captured in smoke and haze scenes immensely. Finally, the enhanced output images can be successfully post-processed for automatic autonomous reading of gauges

Figures (10)

• Figure 1: Realistic rendering of a gauge in a virtual environment (left) and model aspects that are considered when producing the final image (right).
• Figure 2: Two examples from the Synthetic Haze dataset.
• Figure 3: Two examples from the Synthetic Smoke dataset.
• Figure 4: SSIM for FFA-Net and AECR-Net trained on Synthetic Haze dataset.
• Figure 5: PSNR for FFA-Net and AECR-Net trained on Synthetic Haze dataset.