Abstract
Aiming at the problem that the vehicle detection algorithm based on convolutional neural network is too deep in the network layer, resulting in low training efficiency, this paper proposes a visualization method to adjust the structure of convolutional neural network, so as to improve training efficiency and detection effect. Firstly, the existing convolutional neural network model for image classification is visualized using the intermediate layer visualization method of convolutional neural network. Then, the layers of the convolutional neural network model are analyzed to select the layer with the best visualization effect for network reconstruction, so as to obtain a relatively simplified network model. The experimental results show that the similar multi-target detection method proposed in this paper has obvious improvement in training efficiency and accuracy.
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Nanyan, L., Jingyang, Y. (2020). Research on Vehicle Detection Based on Visual Convolution Network Optimization. In: Ben Hedia, B., Chen, YF., Liu, G., Yu, Z. (eds) Verification and Evaluation of Computer and Communication Systems. VECoS 2020. Lecture Notes in Computer Science(), vol 12519. Springer, Cham. https://doi.org/10.1007/978-3-030-65955-4_17
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DOI: https://doi.org/10.1007/978-3-030-65955-4_17
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