Application of one-stage instance segmentation with weather conditions in surveillance cameras at construction sites

On-site surveillance camera systems have been utilized for safety monitoring remotely to reduce occupational accidents and injuries. Even though previous studies for workspace safety monitoring using computer vision have been undertaken, weather-related conditions such as raindrops and snows affecting pan-tilt-zoom camera visibility have not been considered in depth. Developing a robust detector that is reliable in different weather conditions is a challenge for vision-based monitoring outdoor works in construction sites. This study proposes a deep learning-based real time one-stage instance segmentation model for monitoring systems and data augmentation methods to improve the detector's performance under five weather conditions: brightness, darkness, rainy, snowy, and foggy. Experimental results indicate that the proposed model with weather augmentation outperformed the baseline model without augmentation by around 0.05 mAP(05:95). Therefore, this work verified the applicability of the model for detecting and segmenting instances robustly in construction sites. This weather augmentation approach could be applicable to any type of vision-based monitoring system such as quality inspection, productivity assessment, schedule monitoring, and other work management as well as safety management affected by the different weather conditions. There are still room for the future researches in the development of fully autonomous vision-based monitoring systems in the applications mentioned.