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Application of Continuous Learning Algorithm in Vehicle Target Recognition
SUN Jiahui
2023, 48(15):
73-81.
DOI: 10.16638/j.cnki.1671-7988.2023.015.013
Advances in deep learning and artificial intelligence are the main drivers of autonomous
vehicle technology. However, most of the deep learning models are trained on the same staticdistributed data set and their behaviors can not be adaptde or expended over time. To solve this
problem, the continuous learning model is applied in the field of vehicle object recognition. Firstly,
an environment is built to make the algorithm run smoothly, and the original image data set of target
recognition is selected. On the basis of analyzing the existing evaluation indexes, the evaluation
indexes suitable for this experiment are selected, and convolutional neural network (CNN), nearest
class mean (NCM), incremental classifier and representation learning (iCaRL) three continuous learning algorithms are used for learning, training and comparing the original image data set. Based
on the experiment result, the iCaRL algorithm makes the accuracy and efficiency of machine
continuous learning training better than the other two methods. Aiming at the problem that the image
data set of intelligent driving target recognition is not perfect, a new image data set is constructed,
including vehicles, pedestrians, traffic signs and signal lights. ICaRL algorithm is applied to the new
image data set for research, and the new intelligent draving image dataset is trained and tested. The
results show that iCaRL algorithm can learn the new image data in the environment. The test results
show that the method can be used for target recognition in intelligent driving field.
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