Object Detection and Recognition in the Blind Area of the Vehicle Using Deep Learning

• Main Authors: LYU, DENG-YOU, 呂登祐
• Other Authors: Chia, Tsorng-Lin
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/et23cy

碩士 === 銘傳大學 === 資訊傳播工程學系碩士班 === 108 === The vehicle is equipped with mirrors inside and outside, which is the primary tool for driver to observe with the environment around the vehicle. However, the driving blind spots area generated by the vehicle, which has always been the leading cause of traffic accidents. The fisheye camera image could be displayed half-sphere-wide image. In case of the three fisheye cameras installed at different positions outside the vehicle, which might be efficiently catch-all driving visual blind spots area. So through the graphic recognition technology, object recognition for driving blind spots could be carried out to reduce traffic accidents that might be caused by driving blind spots. Due to the fisheye image is close to the periphery, the object in the image will have the higher the distortion. Under the limitation of the object definition, it is difficult to define the object in the blind spots area. This paper employs the convolutional neural network (CNN) method to improve the accuracy of object recognition in the blind zone. For the advantages of the convolution model, this paper employs VGG Net-based and ResNet-based multiple convolution models to achieve merge model research, which could make the identification accuracy to have more accurate for different types of objects appearing in the visual blind zone. Finally, the fisheye imaging experiment of the actual driving is probably used to display the reference more intuitively to achieve the warning function for drivers.