🤖 AI Summary
This study addresses the challenge of low visibility in instrument images caused by smoke and haze, which severely hinders automated meter reading in infrastructure monitoring and emergency response. To this end, the authors construct the first dataset comprising over 14,000 synthetic instrument images, generated using Unreal Engine to simulate realistic smoke and haze degradation. The transferability of two state-of-the-art dehazing networks—FFA-Net and AECR-Net—is systematically evaluated on this dataset. Experimental results demonstrate that AECR-Net achieves superior performance on the synthetic data, attaining an SSIM of 0.98 and a PSNR of 43 dB, significantly enhancing image clarity and thereby effectively supporting downstream automated meter reading tasks.
📝 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.