Training and using methods of driving detection model, training and using devices of driving detection model, equipment and medium

Abstract

The embodiment of the invention discloses training and using methods of a driving detection model, training and using devices of the driving detection model, equipment and a medium. The method comprises: acquiring a training sample, wherein the training sample comprises a driving front vehicle image, and a vehicle marking result and a lane line marking result in the driving front vehicle image; inputting the training sample into a shared extraction sub-model of a driving detection model to obtain shared image features; supervising and training a vehicle detection sub-model and a shared extraction sub-model of the driving detection model by adopting shared image features; and supervising and training the lane line detection sub-model and the shared extraction sub-model of the driving detection model by adopting the shared image features, so as to train the driving detection model. By adopting the scheme of the invention, the vehicle detection sub-model and the lane line detection sub-model are integrated in the same model for supervised training, so that vehicle detection and lane line detection can be realized simultaneously when the trained model is used subsequently.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of automatic driving, and in particular to a training and use method, device, equipment and medium of a driving detection model. Background technique [0002] In the Advanced Driver Assistance System (ADAS), accurate and timely detection of vehicle information and lane line information in front is the key factor to realize the intelligentization of the vehicle assisted driving system. [0003] At present, for vehicle detection problems, deep learning-based methods are generally used to detect the location and type of vehicles ahead. For the detection of lane markings, most of the industry uses traditional methods to detect the position and type of lane markings ahead. However, traditional detection methods are suitable for a single scene, and it is difficult to deal with scenes such as occlusion, blur and bad weather. However, if the method based on deep learning is also used in the de...

AI Technical Summary

Problems solved by technology

For the lane line detection problem, most of the industry uses traditional methods to detect the position and type of the front lane line, but the traditional detection method is suitable for a single scene, and it is difficult to deal with scenes such as occlusion, blur and bad weather.

However, if the method based on deep learning is also used in the detection of lane lines, then running the vehicle detection method based on deep learning and the lane line detection method at the same time will lead to increased detection time and affect the real-time detection

Images