Abstract
An improved Image classification algorithm based on Darknet53 model is proposed in this paper to solve the problem that a large amount of the same gradient information is repeatedly used to update the weights of different dense layers in the Darknet53 network in the process of back propagation. Firstly, the residual block in the original network is replaced by THE CSP module, referring to the idea of cutting off the gradient flow in CSPnet to prevent too much repeated gradient information. Second, the Mish activation function is used to replace the Leaky ReLU function, which can transmit information more smoothly and achieve better accuracy and generalization. Finally, a SPP module is added to the end of the original network structure to solve the multi-scale problem of the main part of image classification. The experimental results show that the improved Darknet has achieved better performance, and the accuracy is improved by 1.3% compared with the original network; At the same time, compared with resnet50 and resnet101, the improved Darknet has better effect on image classification.
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Zou, S., Zhang, B., Zhang, B. (2023). Research on Improved Image Classification Algorithm Based on Darknet53 Model. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2022. Lecture Notes in Electrical Engineering, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-99-2362-5_10
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DOI: https://doi.org/10.1007/978-981-99-2362-5_10
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