| dc.description.abstract | In recent years, in order to reduce the incidence of traffic accidents, governments of different countries have applied an Advanced Driver Assistance Systems (ADAS) to assist drivers and improve driving safety. In 1997, the Taiwan Government has set up a series of safety rules for motorcyclists, passengers and manufacturers. Motorcyclist and rear seat passenger must wear safety helmets when driving, or penalty will be charged. Motorcycles manufactured on or after 2019, must buddle with safety equipment, e.g. anti-lock braking system (ABS) or interlocking brake system (CBS) to improve driving safety. In the sake of safety, many motorcycles have additionally installed a driving recorder, however, which is a passive operation system and unable to provide an active real time detection and warning alert.
Since many car accidents are caused by human factors, e.g. drowsy driving, unfamiliar road conditions, speeding, drunk driving, overtaking, blind corners etc.. Therefore, this research is aimed at alerting motorcyclists from vehicles coming from the rear and surroundings. To reduce the weight of the appliance, the detection and identification system uses embedded devices-Raspberry Pi and NCS 2 Neural Computer Stick 2 (Neural Computer Stick 2), plus a lightweight deep learning model YOLOv3-Tiny. In order to provide an active real time detection and safety driving environment, cameras are installed at the rear of vehicles to detect trucks, buses, cars, and motorcycles, warning will be given to motorcyclists once vehicles are approaching.
Here we used an architecture that does not increase the depth of the network, by adding a Res block module to the 5th and 6th layers to perform feature extraction operations, and using a 960×540 resolution video to bring out the test and comparison. The execution speed of the traditional YOLOv3-Tiny is slightly reduced from 124 fps to 104 fps, mAP is increased from 93.63% to 96.71%.
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