Detection system for backing car into storage and detection method thereof

Abstract

The invention discloses a detection system for backing a car into a store and a detection method thereof. According to the detection system and the detection method thereof, a main camera and an auxiliary camera are used for completely recording a whole dynamic process of backing the car into the store and obtaining the position and the distance of the car relative to a storage location marker line, quantitative analysis of the process of backing the car into the store can be realized through an image analysis meter, and analyzed results are reported to a server for storage, management, and comprehensive grading, and drivers and trainers inquire information or consult video videotapes through a client side of the rear end. Accordingly, the drivers are trained effectively, and study efficiency of driving personnel is improved.

Description

technical field [0001] The invention relates to a detection system and a detection method for reversing a car into a warehouse. Background technique [0002] As a motor vehicle driver, backing into storage is one of the driving skills that must be mastered. However, in traditional driver training, signposts are generally set up around the warehouse, and trainees use the signposts as references for training, resulting in the need to re-cultivate the sense of position and storage skills after the road. Although the position of the vehicle can be collected by installing a certain number of sensors on the road, such as the permanent magnet used in Chinese utility model 201120277707.9, or the infrared rays, lasers, and ultrasonics commonly used in engineering distance measurement, such sensors can only be used at individual fixed points. On-site perception, complex construction and maintenance, and high overall cost, and the linkage of cameras is still required to achieve eviden...

AI Technical Summary

Problems solved by technology

As we all know, this method is difficult to be operable in the real environment, because there are many linear pixels in the vehicle itself, such as windows, ceilings, etc., and there are often a large number of linear pixels in the background environment, such as curbs, In this case, it is difficult to extract the "car" from the background environment at the pixel level, which eventually leads to inaccurate detection

Images