Glaucoma is a chronic eye condition that results in damage to the optic nerve, which leads to permanent blindness if it is not diagnosed and treated early. As of now, there are no treatments for curing glaucoma; thus, early detection might halt its progression. There are several imaging techniques used in detection of glaucoma, and optical coherence tomography (OCT) has gained prominence due to its ability to provide high-resolution images. However, manual observation and diagnosis by ophthalmologists are labor-intensive. This study proposes a novel method for automated detection of glaucoma by using YOLOv5s, which is an object detection model, applied to OCT images of patients both with and without glaucoma. The preliminary results are encouraging and achieved a detection accuracy of 99.3%.