Title: A separable convolutional neural network for vehicle type recognition

Authors: Baili Zhang; Yansu Wang

Addresses: Department of Electronics, Xinzhou Normal University, Xinzhou, Shanxi, 034000, China ' Shanghai Pan Microsoft Parts Co., Ltd., Shanghai, 200000, China

Abstract: 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.

Keywords: vehicle type recognition; GoogLeNet network; TensorFlow framework; separate convolutional neural networks; low-dimensional convolution kernel; HOG_BP algorithm.

DOI: 10.1504/IJCSM.2024.140911

International Journal of Computing Science and Mathematics, 2024 Vol.20 No.2, pp.169 - 176

Received: 27 Dec 2023
Accepted: 30 Apr 2024
Published online: 03 Sep 2024 *

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