The construction industry is one of the largest contributors to the world economy. However, the level of automation and digitalization in the construction industry is still at its infancy in comparison with other industries due to the complex nature and the large size of construction projects. Meanwhile, construction projects are prone to cost overruns and schedule delays due to the adoption of traditional progress monitoring techniques to retrieve progress on-site, having indoor activities participating with an accountable ratio of these works. Improvements in deep learning and Computer Vision (CV) algorithms provide promising results in detecting objects in real time. Also, researchers have investigated the probability of using CV as a tool to create a Digital Twin (DT) for construction sites. This paper proposes a model utilizing the state-of-the-art YOLOv8 algorithm to monitor the progress of bricklaying activities, automatically extracting and analyzing real-time data from construction sites. The detected data is then integrated into a 3D Building Information Model (BIM), which serves as a DT, allowing project managers to visualize, track, and compare the actual progress of bricklaying with the planned schedule. By incorporating this technology, the model aims to enhance accuracy in progress monitoring, reduce human error, and enable real-time updates to project timelines, contributing to more efficient project management and timely completion.