Ensuring construction quality and safety requires timely detection of defects, yet traditional manual inspections are slow, costly, and inconsistent. This research presents a computer vision-driven solution that automates defect detection using images captured by drones and site cameras. By applying advanced deep learning models, the system are expected to identify cracks, corrosion, and surface irregularities with high accuracy, even under challenging site conditions. This helps significantly in the reduction of inspection time and improved reliability compared to manual methods. Beyond technical validation, the approach supports scalable deployment in infrastructure maintenance, smart city projects, and industrial safety compliance, offering a pathway toward more efficient, transparent, and cost-effective construction monitoring.