Glaucoma Detection Using the YOLO V5 Algorithm

High intraocular pressure causes the eye disease glaucoma, which can eventually result in complete blindness. On the other hand, early detection and treatment of glaucoma can prevent complete blindness in a patient. However, we regularly experience delays as a result of challenging glaucoma screening procedures and a shortage of human resources, which might raise the worldwide vision loss ratio. In the final stage, it is envisaged that a confined region comprising glaucoma lesions and associated classes will develop. To prove the technique’s viability, it was put to the test on a challenging dataset, specifically an online retinal fundus image database for glaucoma research (ORIGA). Due to the existing dearth of intelligence and security research on outdoor gantry cranes, a method based on the updated you-only-look-once (YOLO)v5 network for intelligent anti-intrusion detection is proposed. The first step is to offer a broad detection strategy. The YOLOv5 network’s goal is to retain speed while achieving the highest detection precision: Add multi-layer receptive fields and fine-grained modules to the backbone network to improve the performance of features. The training of YOLO V5 resulted in an accuracy of 92.5% by the end of the 100th epoch. The high accuracy hence proves that the model was able to detect effectively.