Using convolutional neural networks for image semantic segmentation and object detection
Convolutional neural networks are widely used for feature extraction in the fields of object detection and image segmentation. However, traditional CNN models often struggle to ensure accuracy in high noise environments. A study proposes an enhanced CNN model to improve its ability to recognize targ...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924001017 |
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| author | Shuangmei Li Chengning Huang |
| author_facet | Shuangmei Li Chengning Huang |
| author_sort | Shuangmei Li |
| collection | DOAJ |
| description | Convolutional neural networks are widely used for feature extraction in the fields of object detection and image segmentation. However, traditional CNN models often struggle to ensure accuracy in high noise environments. A study proposes an enhanced CNN model to improve its ability to recognize targets of different scales. This model combines multi-scale perceptual aggregation and feature alignment (MPAFA) mechanisms. This new method effectively combines low-level and high-level features, which helps to better identify objects of different sizes. The experimental results show that the proposed model achieved a segmentation accuracy of 99.6 % on the Cityscapes dataset, and maintained an accuracy of 97.3 % even with increased noise. Further experiments have shown that the model outperforms existing methods in terms of accuracy and recall. The experimental results show that the model exhibits excellent performance in object detection and segmentation tasks. This study provides a more effective strategy for processing complex images by optimizing network structure and enhancing feature fusion. |
| format | Article |
| id | doaj-art-3e9bd92bdb8c48db8c588f91666b2105 |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-3e9bd92bdb8c48db8c588f91666b21052025-08-20T01:58:33ZengElsevierSystems and Soft Computing2772-94192024-12-01620017210.1016/j.sasc.2024.200172Using convolutional neural networks for image semantic segmentation and object detectionShuangmei Li0Chengning Huang1Corresponding author.; School of Computer and Communication Engineering, Nanjing Tech University Pujiang Institute, Nanjing, 211200, PR ChinaSchool of Computer and Communication Engineering, Nanjing Tech University Pujiang Institute, Nanjing, 211200, PR ChinaConvolutional neural networks are widely used for feature extraction in the fields of object detection and image segmentation. However, traditional CNN models often struggle to ensure accuracy in high noise environments. A study proposes an enhanced CNN model to improve its ability to recognize targets of different scales. This model combines multi-scale perceptual aggregation and feature alignment (MPAFA) mechanisms. This new method effectively combines low-level and high-level features, which helps to better identify objects of different sizes. The experimental results show that the proposed model achieved a segmentation accuracy of 99.6 % on the Cityscapes dataset, and maintained an accuracy of 97.3 % even with increased noise. Further experiments have shown that the model outperforms existing methods in terms of accuracy and recall. The experimental results show that the model exhibits excellent performance in object detection and segmentation tasks. This study provides a more effective strategy for processing complex images by optimizing network structure and enhancing feature fusion.http://www.sciencedirect.com/science/article/pii/S2772941924001017Convolutional neural networkFeature alignmentSpatial attentionSemantic segmentationObject detection |
| spellingShingle | Shuangmei Li Chengning Huang Using convolutional neural networks for image semantic segmentation and object detection Systems and Soft Computing Convolutional neural network Feature alignment Spatial attention Semantic segmentation Object detection |
| title | Using convolutional neural networks for image semantic segmentation and object detection |
| title_full | Using convolutional neural networks for image semantic segmentation and object detection |
| title_fullStr | Using convolutional neural networks for image semantic segmentation and object detection |
| title_full_unstemmed | Using convolutional neural networks for image semantic segmentation and object detection |
| title_short | Using convolutional neural networks for image semantic segmentation and object detection |
| title_sort | using convolutional neural networks for image semantic segmentation and object detection |
| topic | Convolutional neural network Feature alignment Spatial attention Semantic segmentation Object detection |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924001017 |
| work_keys_str_mv | AT shuangmeili usingconvolutionalneuralnetworksforimagesemanticsegmentationandobjectdetection AT chengninghuang usingconvolutionalneuralnetworksforimagesemanticsegmentationandobjectdetection |