Electric Bicycle Violation Automatic Detection in Unconstrained Scenarios. Object detection technology develops rapidly and has broad application prospects. There are few relevant researches on the detection of electric bicycle violation. It is of great practical significance to apply the object detection technology to the detection of electric bicycle violation. The main violations of electric bicycle are not wearing safety helmet, not install license plate, overload and so on. Use YOLOv3 to train the datasets of riding electric bike, safety helmet and license plate to detect electric bicycle violations; The technology of chineseocr is used to identify the electric bicycle license plate number. The experiment proves that the method presented in this paper has a high detection accuracy for the objects of riding electric bike, safety helmet and license plate, but the recognition accuracy for license plate number is a little less.

Development of Smart Plate Number Recognition System for Fast Cars with Web Application Traffic law violation has been recognized as a major cause for road accidents in most parts of the world with majority occurring in developing countries. Even with the presence of rules and regulations stipulated against this, violators are still on the increase. This is due to the fact that the rules are not properly enforced by appropriate authorities in those parts of the world. Therefore, a system needs to be designed to assist law enforcement agencies to impose these rules to improve road safety and reduce road accidents. This work uses a Vehicle Plate Number Recognition (VNPR) system which is a realtime embedded system to automatically recognize license plate numbers. It provides an alternative means to VPNR using an opensource library known as openCV. The main aim of the system is to use image processing to identify vehicles violating traffic by their plate numbers. It consists of an IR sensor for detecting the vehicle. During testing, a minimum time was set for the sensor to detect the object which was recorded by the microprocessor. Once it was less than the set time, the camera was triggered to capture the plate number and store the image on the Raspberry Pi. The image captured is processed by the Raspberry Pi to extract the numbers on the image. The numbers on the capture imaged were viewed on a web page via an IP address. The system if implemented can be used to improve road safety and control traffic of emerging smart cities. It will also be used to apply appropriate sanctions for traffic law violators.