Safety is a main concern for the construction industry because of the high rate of accidents and casualties on construction sites. Personal Protective Equipment (PPE) is a major part of safety regulations to prevent accidents. However, workers may neglect to wear the required PPE while working, which subsequently increases the potential risk for accidents. Currently, safety managers and inspectors on construction sites are responsible for monitoring safety regulations, which is a time-consuming task. To facilitate safety monitoring, a large number of research studies applied computer vision for detecting PPE on construction sites. Nevertheless, detecting workers and PPE is still a challenge in far-field videos. This research proposes an approach for detecting if anyone on the construction site is wearing the required PPE, even when he or she is far from the surveillance cameras. This method uses a frame segmentation technique and a nested network with two Faster R-CNN models to detect safety noncompliances. The first model detects the human bodies on the construction site, and the second one detects if the detected person is wearing a hardhat and a safety vest. The proposed method is applied to videos from a construction site. The experimental results demonstrate the practicality and robustness of the proposed method to detect PPE in far-field videos. Based on three different test videos, the average precision and recall for the worker detection model were 99.67% and 92.92%, respectively. The PPE detection model had the average precision and recall of 91.25% and 94.77%, respectively.