HELMET DETECTION AND LICENSE PLATE RECOGNITION USING YOLO MODEL

Information Technology

In today’s world, the increasing use of Motorcycles has prompted increment in road accidents and injuries. Helmet not used by the motorcycle rider is one of the major causes. Currently, one procedure is to physically check use of helmet at the pavement junction or through the CCTV footage video, which requires human energy to detect motorcyclists without helmet. The object detection and tracking is the important steps of computer vision algorithm. The robust object detection is the challenge due to variations in the scenes. Another biggest challenge is to track the object in the occlusion conditions. Hence in this approach, the moving objects detection using OpenCV object detection API. Further the location of the detected object is pass to the object tracking algorithm. A YOLO object tracking algorithm is used for robust object detection. The proposed approach is able to detect the object in different illumination and occlusion. The system extracts objects class based on feature extracted. The system uses You Only Look Once (YOLO)-Darknet deep learning framework which consists of Convolutional Neural Networks trained on Common Objects in Context (COCO) and combined with computer vision.

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