AUTOMATIC VEHICLE NUMBER IDENTIFICATION USING DIGITAL IMAGE PROCESSOR(DIP)

ELECTRICAL ENGINEERING

Road accidents are one of the major causes of human deaths. Most of the deaths in accidents are due to damage to the head of the motorcycle riders. Among the different types of road accidents, motorcycle accidents are common and cause severe injuries. To reduce the involved risk for the motorcycle riders, it is exceptionally fascinating to utilize helmet. The helmet is the motorcyclist’s main protection. Most countries require the use of helmets by motorcyclists, but many people fail to obey the law for various reasons. We present the development of a system using deep convolutional neural networks (CNNs) for finding motorcyclists who are violating helmet laws. The system comprises motorcycle detection, helmet vs. no-helmet classification and counting process. Faster R-CNN with ResNet 50 network model is implementing for motorcycle detector process. CNN classification model proposes for classify the helmet vs. no-helmet. Finally making alarm sound to alert the officer to preventing motorcycle accident. We evaluate the system in terms of accuracy and speed and identify the number plate of the vehicle

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