Identifying Bikers Without Helmets Using Deep Learning Models

Inspired by the recent progress in Computer Vision, we introduce a real-time smart surveillance system which uses Computer Vision and Deep Learning algorithms to identify bikers without helmets and retrieves registration numbers from detected license plates using Tesseract OCR along with necessary Computer Vision techniques and libraries. The video dataset was collected from the busiest roads of Dhaka, Bangladesh in 720p HD resolution at 30 fps. Deep Learning framework Tensorflow's SSD Mobilenet V2 and Faster R-CNN inception V2 models were used for object detection. We validated the use of our system on our dataset which gave 90%, 55%, 80%, 95% accuracy for helmet, human, bike and number plate respectively in SSD Mobilenet V2 and 92%, 58%, 81%, 96% for helmet, human, bike and number plate respectively in Faster RCNN inception V2. The number plate recognition has an accuracy of 98%. The retrieved registration numbers are then stored in a database for further identification of the bikers without helmets. The proposed system outperforms other related real-time helmet detection systems and license plate recognition models. The system achieved a high frames per second(FPS) rate of approximately 45 on NVIDIA RTX2080 GPU and was able to perform successfully even when there were 6 bikes in a frame. Another contribution is that, our dataset has a high biker density per frame and 5626 images were labeled with 24465 bounding boxes. The dataset can be used for further real-time surveillance system research effectively.