Lane and object detection algorithm based on deep learning

Biao Liu; Guohao Liu; Junchao Qiao

doi:10.1117/12.2641319

10 November 2022 Lane and object detection algorithm based on deep learning

Biao Liu, Guohao Liu, Junchao Qiao

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481N (2022) https://doi.org/10.1117/12.2641319
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

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%.

Citation Download Citation

Biao Liu, Guohao Liu, and Junchao Qiao "Lane and object detection algorithm based on deep learning", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481N (10 November 2022); https://doi.org/10.1117/12.2641319

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