Detection and Classification of ‘Myopia Epidemic’ Using Image Processing and Machine Learning to Prevent Fatal Road Accidents

Poor vision is one of the Biggest Issue faced by Indian Drivers. 80 percent of deaths on Indian roads are due to driver errors. According to NCRB reported Data Road Accidents are increased from 3,68,828 in 2020 to 4,22,659 in 2021. Individuals with poor vision are more likely to be involved in a highway road accident. Our study noted that one in three drivers needed vision correction urgently, while another safety audit found that 5% of driver were suffering from myopia cataract. Myopia is a Nearsightedness it is a condition in which nearby objects appear clearly but distant objects looks blurry. The Unaware commercial drivers are need to be monitored by traffic Departments. Hence, the traffic enforcement departments like police departments and the Road Transport Offices (RTO) need an equipment like a device being used for drunk and drive identification. By Using this device, we can identify drivers, especially truck and bus drivers with poor vision, and provide them with prompt treatment to ensure their safety. In our project the drivers eye image is captured using the 3d stereoscopy technique. Then the Images are converted into Fundus Images for a 2d representation. The provided framework includes a representation of the biopsy procedures. Tiny myopia pandemic biopsy images are in RGB design and are converted to black and white images. Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) techniques are used to segment dim scale images for surface extraction, obtaining surface characteristics for differentiation, relationship, vitality, and homogeneity highlights, respectively. Using a Convolutional Neural Network (CNN) calculation, images are divided into two classes: Myopic and Non-Myopic. Analysis of little sputum tests for the disease associated with the myopia epidemic and the detection of myopia epidemic malignancy protests in CT sweep. The Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) techniques have been used to demonstrate the myopia epidemic malignancy using CNN Algorithm. The ML algorithm is used to classify the level of treatments required and give advises to the enforcement departments to the kind of action remedial actions to be taken. Finally, it is decided that the person need surgery or a corrective lens, and these suggestions are made.