Deep Learning Approaches for Traffic Sign Detection and Recognition
Pages : 627-631
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Abstract
Traffic sign recognition is used to maintain traffic signs, warns the distracted driver, and prevent his/her actions that can lead an accident. A real-time automatic sign detection and recognition can help the driver, significantly increasing his/her safety. Traffic sign recognition also gets an immense interest lately by large scale companies such as Google, Apple and Volkswagen etc. driven by the market needs for intelligent applications such as autonomous driving, driver assistance systems (ADAS), mobile mapping, Mobil eye, Apple, etc. and datasets such as Belgian, German mobile mapping. Hence, in this paper ,we are proposing to do the same with cost efficient manner using Raspberry Pi. We are proposing automated real time system which will capture traffic sign and indicate it at driver dashboard with front obstacle exact distance on monitor. PiCam is used to capture images of traffic sings and is connected to RaspberryPi. Monitor is used to display required output, showing type of sign and distance of collision. This proposal will avoid large number of accidents occurring at bridges and work in progress area due to automated braking system and simultaneous reduce death ratio.
Keywords: PiCAM, Raspberry Pi, Ultrasonic sensors, Traffic Sign recognition.