Lane and object detection are important tasks in autonomous driving environment awareness system, and also the basis of vehicle path planning, decision making and control. Convolution neural networks are widely used in various visual detection tasks, providing a new approach for intelligent vehicle visual perception system. Based on deep learning technology, this paper designs a feature fusion module: Cross Convolution module, which is used to improve the accuracy of lane detection. In this paper, a multi-task convolution neural network is proposed to realize lane detection and object detection simultaneously. The network has the characteristics of small number of parameters and low computational cost. Real road images are collected by real vehicles to train the algorithm. Compared with single-task network, the overall detection speed of the model is improved by 61.77% when mAP decreases by 0.83%.
With the development of intelligent driving technology, the progress of hardware technology. Visual perception technology based on deep learning has been applied more and more in the field of intelligent driving. Vision, as the main part of information acquisition, is the core of automatic assisted driving technology. Based on the intelligent vehicle as the research platform, the use of ROS combined the technology of deep learning design implements a lane detection algorithm, in the car on the road ahead uninterrupted detection, extraction of the road lane information, to guide the car driving direction, effectively improve the security and intelligent vehicle driving process, to achieve the function of unmanned vehicle automated driving laid the foundation.
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