Abstract
Convolutional neural network has attracted increasing attention in object detection and recognition. Among the methods mentioned in various literatures, the emphasis is to increase the depth of the model so as to improve the accuracy of recognition and detection. However, these large networks require more computing overhead and memory storage, which restricts their usages on mobile devices. Inspired by the superior detection performance of residual network (ResNet), we proposed a lightweight network, called SlimResNet, which is applied in fabric defect detection. Firstly, due to the particularity of fabric defects, the size of residual network convolution kernel will be changed in this paper to capture more details. Secondly, since the high-level semantic information of convolutional neural network is redundant for defect detection, we pruned the network structure to better compress the network model. Finally, the experimental results show that the recognition performance of the proposed lightweight network is comparable to the original network.
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Acknowledgement
This work was supported by NSFC (No. 61772576, U1804157), Science and technology innovation talent project of Education Department of Henan Province (17HASTIT019), The Henan Science Fund for Distinguished Young Scholars (18410 0510002), Henan science and technology innovation team (CXTD2017091), IRTSTHN (18IRTSTHN013), Program for Interdisciplinary Direction Team in Zhon-gyuan University of Technology.
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Liu, X., Liu, Z., Li, C., Dong, Y., Wei, M. (2020). SlimResNet: A Lightweight Convolutional Neural Network for Fabric Defect Detection. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_50
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DOI: https://doi.org/10.1007/978-981-15-3415-7_50
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