LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products
The remarkable success of convolutional neural networks (CNNs) in computer vision tasks is shown in large-scale datasets and high-performance computing platforms. However, it is infeasible to deploy large CNNs on resource constrained platforms, such as embedded devices, on account of the huge overhe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3620 |
| _version_ | 1797532998310035456 |
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| author | Na Qin Longkai Liu Deqing Huang Bi Wu Zonghong Zhang |
| author_facet | Na Qin Longkai Liu Deqing Huang Bi Wu Zonghong Zhang |
| author_sort | Na Qin |
| collection | DOAJ |
| description | The remarkable success of convolutional neural networks (CNNs) in computer vision tasks is shown in large-scale datasets and high-performance computing platforms. However, it is infeasible to deploy large CNNs on resource constrained platforms, such as embedded devices, on account of the huge overhead. To recognize the label numbers of industrial black material product and deploy deep CNNs in real-world applications, this research uses an efficient method to simultaneously (a) reduce the network model size and (b) lower the amount of calculation without compromising accuracy. More specifically, the method is implemented by pruning channels and corresponding filters that are identified as having a trivial effect on the output accuracy. In this paper, we prune VGG-16 to obtain a compact network called LeanNet, which gives a 25× reduction in model size and a 4.5× reduction in float point operations (FLOPs), while the accuracy on our dataset is close to the original accuracy by retraining the network. Besides, we also find that LeanNet could achieve better performance on reductions in model size and computation compared to some lightweight networks like MobileNet and SqueezeNet, which are widely used in engineering applications. This research has good application value in the field of industrial production. |
| first_indexed | 2024-03-10T11:08:22Z |
| format | Article |
| id | doaj.art-7466fed7818e49cdaaadbb5bd9884788 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:08:22Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7466fed7818e49cdaaadbb5bd98847882023-11-21T20:57:48ZengMDPI AGSensors1424-82202021-05-012111362010.3390/s21113620LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial ProductsNa Qin0Longkai Liu1Deqing Huang2Bi Wu3Zonghong Zhang4The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaThe Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaThe Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaThe Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaThe Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaThe remarkable success of convolutional neural networks (CNNs) in computer vision tasks is shown in large-scale datasets and high-performance computing platforms. However, it is infeasible to deploy large CNNs on resource constrained platforms, such as embedded devices, on account of the huge overhead. To recognize the label numbers of industrial black material product and deploy deep CNNs in real-world applications, this research uses an efficient method to simultaneously (a) reduce the network model size and (b) lower the amount of calculation without compromising accuracy. More specifically, the method is implemented by pruning channels and corresponding filters that are identified as having a trivial effect on the output accuracy. In this paper, we prune VGG-16 to obtain a compact network called LeanNet, which gives a 25× reduction in model size and a 4.5× reduction in float point operations (FLOPs), while the accuracy on our dataset is close to the original accuracy by retraining the network. Besides, we also find that LeanNet could achieve better performance on reductions in model size and computation compared to some lightweight networks like MobileNet and SqueezeNet, which are widely used in engineering applications. This research has good application value in the field of industrial production.https://www.mdpi.com/1424-8220/21/11/3620convolutional neural networkimage classificationnetwork pruningMobileNetSqueezeNet |
| spellingShingle | Na Qin Longkai Liu Deqing Huang Bi Wu Zonghong Zhang LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products Sensors convolutional neural network image classification network pruning MobileNet SqueezeNet |
| title | LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products |
| title_full | LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products |
| title_fullStr | LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products |
| title_full_unstemmed | LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products |
| title_short | LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products |
| title_sort | leannet an efficient convolutional neural network for digital number recognition in industrial products |
| topic | convolutional neural network image classification network pruning MobileNet SqueezeNet |
| url | https://www.mdpi.com/1424-8220/21/11/3620 |
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