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

Kurzfassung

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 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 were unavailable, two synthetic datasets are generated using the Unreal Engine: a synthetic haze (approx. 4800 images) and a synthetic smoke (approx. 9600 images). Two datasets and two deep-learning frameworks allow the investigation of four different models. The models are trained with an 80% train, 10% validation, and 10% test split for the haze and smoke dataset, respectively. As a result, more robust results are retrieved from the AECR-Net, when compared to FFA-Net and to a prior-based model. For instance, for the synthetic haze dataset, the SSIM and PSNR metrics are about 0.98 and 43 dB obtained with AECR-Net, respectively, comparing well to state-of-the art results. The results from the synthetic smoke dataset are poorer, however the trained models still 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.