Real Time Detection Algorithm of Parking Slot Based on Deep Learning and Fisheye Image

Abstract

Parking slot detection is the basic part of environment perception in automatic parking system. How to detect parking slot accurately and effectively is a key problem that has not been solved in automatic parking system. In order to make up for the defects of parking slot detection based on ultrasonic radar and solve the problems of low recognition rate, sensitivity to environmental changes and weak generalization ability brought by vision-based parking slot detection method. In this paper, a method based on the deep convolution neural network is proposed to detect parking slot. The algorithm takes the fisheye image collected by the four-way fisheye camera mounted on the car body as the input and adopts the improved YoloV3 network structure to detect parking slot directly in the fisheye image. The experimental results show that the recall ratio of the method is 98.72% and the accuracy is 99.14% on the self-made parking data set. The algorithm has achieved good results in real vehicle vehicle environment and can achieve real-time detection.