Image Processing

The paper assesses the effectiveness of sharpness metrics for monitoring the cleanliness of in-line thermographic systems and enabling self-diagnosis, in order to prevent degradation of metrologic performance and increase of measurement uncertainty. When optical measurement systems are installed in harsh industrial environments, external contaminants may compromise their operation conditions. Various types of dust may settle on the lens or protective window, deteriorating the quality of the generated signal.

The data used in this study was gathered from various practical applications, specifically sourced from an access control device that incorporates a hardware module. Meanwhile, the data acquired through sampling conducted under 850nm light. The hardware device itself was utilized in diverse scenarios, encompassing indoor and outdoor environments, with and without background influences. Users participated in the data collection process by placing their palms naturally onto the device for multiple captures.

This dataset contains simulated images and luminance matrix data of direct-lit automotive interior light guides. The light guide designs were modeled using optical simulation software, and the resulting images and luminance distributions were exported for analysis. The dataset includes:

Despite the existence of road image datasets, these datasets predominantly focus on European roads with less variability in traffic and road conditions. To address this limitation, we have developed an image dataset tailored to Indian road conditions, capturing the extensive variations in traffic and environment.