The traditional vehicle type recognition algorithm has a low image recognition rate for various vehicle types on diverse road conditions and is prone to being affected by shooting distance, light intensity, and weathe...
详细信息
The traditional vehicle type recognition algorithm has a low image recognition rate for various vehicle types on diverse road conditions and is prone to being affected by shooting distance, light intensity, and weather. To address these problems, a new separate convolutional neural network structure was proposed to automatically classify the images of different vehicle types based on the deep learning TensorFlow framework and the classical GoogLeNet-based network model. Experimental results on the data sets of BIT-Vehicle and Cars-196 show that, compared with the traditional hog_bp algorithm and convolutional neural network model, the decomposed convolutional neural network has a higher recognition rate for the same difficult vehicle images, and its average accuracy rate reaches 96.30%. In addition, the adjustment of hyperparameters in the network ensures that the parameters, such as weight and bias amount, are more efficient and reasonable when constantly updated.
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