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- W4308702496 abstract "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%." @default.
- W4308702496 created "2022-11-14" @default.
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- W4308702496 date "2022-11-11" @default.
- W4308702496 modified "2023-10-14" @default.
- W4308702496 title "Lane and object detection algorithm based on deep learning" @default.
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- W4308702496 doi "https://doi.org/10.1117/12.2641319" @default.
- W4308702496 hasPublicationYear "2022" @default.
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