An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network

Abstract

Detection of helmeted and non-helmeted motorcyclist is mandatory now-a-days in order to ensure the safety of riders on the road. However, due to many constraints such as poor video quality, occlusion, illumination, and other varying factors it becomes very difficult to detect them accurately. In this paper, we introduce an approach for automatic detection of helmeted and non-helmeted motorcyclist using convolutional neural network (CNN). During the past several years, the advancements in deep learning models have drastically improved the performance of object detection. One such model is YOLOv2 [1] which combines both classification and object detection in a single architecture. Here, we use YOLOv2 at two different stages one after another in order to improve the helmet detection accuracy. At the first stage, YOLOv2 model is used to detect different objects in the test image. Since this model is trained on COCO dataset, it can detect all classes of the COCO dataset. In the proposed approach, we use detection of person class instead of motorcycle in order to increase the accuracy of helmet detection in the input image. The cropped images of detected persons are used as input to second YOLOv2 stage which was trained on our dataset of helmeted images. The non-helmeted images are processed further to extract license plate by using OpenALPR. In the proposed approach, we use two different datasets i.e., COCO and helmet datasets. We tested the potential of our approach on different helmeted and non-helmeted images. Experimental results show that the proposed method performs better when compared to other existing approaches with 94.70% helmet detection accuracy.