A neural network real time recognition system for helmet wearing identification at motorbike waiting zones by using surveillance

• Main Authors: Jun Jie Yang, 楊竣傑
• Other Authors: Shih-Hsiung Twu
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/499v8k

碩士 === 中原大學 === 通訊工程碩士學位學程 === 107 === Abstract In this thesis, we will propose a method to use the pictures of surveillance camera to recognize whether the riders correctly wearing the helmet in the motorbike waiting zones. This thesis is divided into three parts. In the first part, we use the video obtained by the monitor to capture a single image or the whole image and reduce the pixel motion. To make the identification more precise, we use the software label Image in the form of supervised learning, the labels are classified into three types in each photo: unfastened, buckled, and unbuckled. In the second part, we input the processed training data into the Faster CNN model of the neural network, and automatically extract the convolution features through the RPN and feature maps and enter the ROI pooling layer to obtain the bounding box of the same size. Then through the full connection layer into the regressor layer and softmax layer and then generate the classification probability and the position of the bounding box. By the complex automatic iterative training to automatically distinguish the three types of buckles, and thus create a real-time image recognition of the helmet buckle system. The final part is the experimental simulation results. We will observe that the system can achieve our expected results in different locations and with different types of helmets. In this thesis, we have some contributions as follows: 1. Security: Providing law enforcement units to use the surveillance system''s instant image path to ban, can reduce the risk of law enforcement officers on the first line of law enforcement, and after this ban, improve the proportion of motorcycle riders wearing helmets correctly, thereby reducing the death rate from car accidents. 2. Low cost: Owing to very dense features of the use of monitor systems on urban roads, use this system to replace manual enforcement, which reduces a lot of labor costs. 3. Trending: With the advancement of technology, in the Internet of Things and the generation of artificial intelligence, we will combine monitoring systems with automated identification systems to achieve technological enforcement.