Real-Time Blind Spot Vehicle Detection and Risk Classification System Using Deep Neural Network

This study is about a way to detect things around autonomous vehicles in real time. It uses a two step process. First it finds objects using YOLOv8. Then it figures out what those objects are using other methods like EfficientNetV2, MobileNetV2, VGG16 and ResNet-50. The study used a lot of pictures of roads around 9,850 of them to make this system work. EfficientNetV2 was the best at getting it right it was correct 91 percent of the time and it was very good at finding things that could be dangerous. This system is as good as or even better than systems that use radar. It only needs one low cost camera. The study also looked at how these methods work for detecting blind spots, which is a big problem on highways in Bangladesh where there are a lot of different types of vehicles on the road and accidents happen because people cannot see what is, around them.