Research On Seat Blet Identification Based On Vehicle-mounted Machine Vision

In order to increase the wearing rate of seat belt, influence of different3point seat belt wearing patterns on occupant injury mechanism is analyzed, due to the existing problems in seat belt utilization. An evaluation criteria of seat belt identification is proposed by identifying seat belt with vehicle-mounted machine vision. An identification model is built to meet the requirements of real time and high precision, embedded vehicle-mounted seat belt identification system is designed and realized. The main research content is as follows:1) Investigation and analysis of3point seat belt utilization. Investigations were launched in typical cities in China. The results show that, problems existing in using seat belt mainly are:non-standard wearing and curly wearing. Non-standard wearing patterns that cause the failure of seat belt identification system includes:using only seat belt buckle, pre-wearing seat belt and using only shoulder belt. Curly wearing patterns includes:shoulder belt curling, waist belt curling and severely curling.2) Influence of different3point seat belt wearing patterns on occupant injury mechanism:the occupant restraint system model is built based on MADYMO software, and its validity is verified. Simulation on different seat belt wearing patterns is conducted. The simulation results demonstrate that:in the event of vehicle crash, the occupant without seat belt will be thrown out of the seat; the occupant wearing only the shoulder belt will have obvious diving and gliding movement. Compared with the situation when three-point seat belt are used correctly, curly wearing will cause a significant increase in occupant injury criterion. Therefore, identifying different wearing patterns has a positive impact on the protection function of the seat belt.3) Vehicle-mounted video monitoring system platform construction and test design. Vehicle-mounted video monitoring system platform is built, research is conducted on CCD sensor performance parameter, infrared light device and seat belt of special material. Vehicle field driving tests in different lighting environments are designed and conducted, occupant image information of different seat belt wearing patterns is collected. Image preprocessing technology appropriate for seat belt identification is studied in order to lay the foundation of building multiple characteristic parameter model for seat belt identification.4) Real-time seat belt identification modeling. Aimed at the requirement of seat belt on-line test, space parameter characteristic after dimensionality reduction based on PCA (principal component analysis) is selected as input vector, BP neural network is chosen to be the classifier to meet the requirement of real-time identification, genetic algorithm (GA) is brought in to optimize the internal parameters to improve accuracy. At last, a real-time seat belt identification model based on GA-BP neural network is built. The real-time performance and accuracy of this model are verified through hardware-in-loop (HIL) and model-in-loop (MIL).5) Curly wearing identification model with high accuracy is established. Statistical characteristic value of seat belt structural parameters is extracted as input vector, support vector machine (SVM) is chosen as the core model classifier, internal parameters are selected by cross validation method and optimized by particle swarm optimization (PSO), an identification model with high precision based on PSO-SVM method is built. The validation of the code in this model is verified through software-in-loop test, so that it can be applied to high accuracy offline testing.6) Embedded vehicle-mounted seat belt identification system is designed and realized. The requirement of vehicle-mounted system on hardware and software is analyzed, high-speed data processing in DSP kernel, control and management fuction of ARM kernel are studied. ICETEK-DM642-B evaluation board is chosen as the hardware platform, and the seat belt identification system is realized based on embedded technology. System function overall design and function module software design are achieved, system program is optimized.The innovations of this paper are as follows:1) A method of seat belt identification based on machine vision is proposed for the fist time, embedded vehicle-mounted seat belt identification system platform is built.2) The relationship between seat belt wearing patterns (non-standard wearing and curly wearing) and occupant injury is revealed. 3) The criterion to evaluate real-time performance and accuracy in seat belt identification is proposed.4) The real-time seat belt identification model based on GA-BP neural network is built.5) The curly wearing identification model with high accuracy based on PSO-SVM is built.