Image Processing

Accurately detecting power line defects under diverse weather conditions is crucial for ensuring power grid reliability and safety. Existing power line inspection datasets, while valuable, often lack the diversity needed for training robust machine learning models, particularly for adverse weather scenarios like fog, rain, and nighttime conditions. This paper addresses this limitation by introducing a novel framework for generating synthetic power line images under diverse weather conditions, thereby enhancing the diversity and robustness of power line inspection systems.

The Facial Expression Dataset (Sri Lankan) is a culturally specific dataset created to enhance the accuracy of emotion recognition models in Sri Lankan contexts. Existing datasets, often based on foreign samples, fail to account for cultural differences in facial expressions, affecting model performance. This dataset bridges that gap, using high-quality data sourced from over 100 video clips of professional Sri Lankan actors to ensure expressive and clear facial imagery.

In recent years, the development of driver assistance technology has become a major focus in the automotive industry, particularly in enhancing road detection systems with informative features about the driving environment. These systems aim to provide navigation and improve driving safety. It is crucial for these systems to accurately recognize and understand road environments, especially marked and unmarked roads.

Recent advances in generative visual content have led to a quantum leap in the quality of artificially generated Deepfake content. Especially, diffusion models are causing growing concerns among communities due to their ever-increasing realism. However, quantifying the realism of generated content is still challenging. Existing evaluation metrics, such as Inception Score and Fréchet inception distance, fall short on benchmarking diffusion models due to the versatility of the generated images.