Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition
Convolutional neural networks typically employ convolutional layers for feature extraction and pooling layers for dimensionality reduction. However, conventional pooling methods often lead to a loss of critical feature information, particularly in images with diverse content, such as vehicle images....
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/24/3885 |
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| author | Cheng-Jian Lin Bing-Hong Chen Chun-Hui Lin Jyun-Yu Jhang |
| author_facet | Cheng-Jian Lin Bing-Hong Chen Chun-Hui Lin Jyun-Yu Jhang |
| author_sort | Cheng-Jian Lin |
| collection | DOAJ |
| description | Convolutional neural networks typically employ convolutional layers for feature extraction and pooling layers for dimensionality reduction. However, conventional pooling methods often lead to a loss of critical feature information, particularly in images with diverse content, such as vehicle images. This study proposes a novel approach to address these problems: a convolutional neural network with type-2 fuzzy-based pooling (CNN-T2FP). This innovative pooling method utilizes type-2 fuzzy membership functions to effectively manage local imprecision in feature maps. Compared with type-1 fuzzy pooling, which only addresses uncertainty to a certain extent, type-2 fuzzy pooling exhibits improved adaptability to different image contents. The experimental results of this study revealed that the CNN-T2FP achieved average accuracies of 92.14% and 93.34% on two datasets, surpassing the performance of existing pooling techniques. In addition, t-distributed stochastic neighbor embedding plots and feature visualization maps further highlighted the potential of type-2 fuzzy-based pooling to overcome the limitations of conventional pooling methods and enhance the performance of convolutional neural networks in image analysis tasks. |
| format | Article |
| id | doaj-art-1068a4fa3544427b8fea9788753dae17 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-1068a4fa3544427b8fea9788753dae172025-08-20T02:50:41ZengMDPI AGMathematics2227-73902024-12-011224388510.3390/math12243885Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle RecognitionCheng-Jian Lin0Bing-Hong Chen1Chun-Hui Lin2Jyun-Yu Jhang3Ph.D. Program, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung 411, TaiwanPh.D. Program, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 404, TaiwanConvolutional neural networks typically employ convolutional layers for feature extraction and pooling layers for dimensionality reduction. However, conventional pooling methods often lead to a loss of critical feature information, particularly in images with diverse content, such as vehicle images. This study proposes a novel approach to address these problems: a convolutional neural network with type-2 fuzzy-based pooling (CNN-T2FP). This innovative pooling method utilizes type-2 fuzzy membership functions to effectively manage local imprecision in feature maps. Compared with type-1 fuzzy pooling, which only addresses uncertainty to a certain extent, type-2 fuzzy pooling exhibits improved adaptability to different image contents. The experimental results of this study revealed that the CNN-T2FP achieved average accuracies of 92.14% and 93.34% on two datasets, surpassing the performance of existing pooling techniques. In addition, t-distributed stochastic neighbor embedding plots and feature visualization maps further highlighted the potential of type-2 fuzzy-based pooling to overcome the limitations of conventional pooling methods and enhance the performance of convolutional neural networks in image analysis tasks.https://www.mdpi.com/2227-7390/12/24/3885vehicle recognitiontype-2 fuzzy setconvolutional neural networkpooling operation |
| spellingShingle | Cheng-Jian Lin Bing-Hong Chen Chun-Hui Lin Jyun-Yu Jhang Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition Mathematics vehicle recognition type-2 fuzzy set convolutional neural network pooling operation |
| title | Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition |
| title_full | Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition |
| title_fullStr | Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition |
| title_full_unstemmed | Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition |
| title_short | Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition |
| title_sort | design of a convolutional neural network with type 2 fuzzy based pooling for vehicle recognition |
| topic | vehicle recognition type-2 fuzzy set convolutional neural network pooling operation |
| url | https://www.mdpi.com/2227-7390/12/24/3885 |
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