Traffic instruction localization algorithm based on reinforcement learning
As a key technology in the field of unmanned driving,traffic indication positioning directly affects the driving safety of automobiles.Using the reinforcement learning algorithm Q-learning,combined with convolutional neural networks,a model for traffic scenarios is proposed,which allows the agent to focus its attention on the candidate region,learn simple operations to transform the bounding box to determine the correct position of the target,and evaluate it on the LaRA semaphore dataset.The results show that un-der the guidance of the proposed model,the agent can detect traffic lights after analyzing the area in the image and obtain the best de-tection results.
Object detvectionReinforcement learningConvolutional neural networksAgent training
国家科技期刊平台
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