Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance
Although traditional convolutional neural networks (CNN) have been significantly improved for target detection, they cannot be completely applied to objects with occlusions in commodity detection. Therefore, we propose a target detection method based on an improved YOLOv5 model and an improved atten...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10225039/ |
| _version_ | 1797733222907379712 |
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| author | An Xie Kai Xie Hao-Nan Dong Jian-Biao He |
| author_facet | An Xie Kai Xie Hao-Nan Dong Jian-Biao He |
| author_sort | An Xie |
| collection | DOAJ |
| description | Although traditional convolutional neural networks (CNN) have been significantly improved for target detection, they cannot be completely applied to objects with occlusions in commodity detection. Therefore, we propose a target detection method based on an improved YOLOv5 model and an improved attention mechanism algorithm is proposed to solve the commodity occlusion problem. This method improves the traditional YOLO deep convolution network, features a more detailed BiFPN layer, and performs lightweight two-way feature fusion, where the multidimensional features of the commodities are convolved and fused, thus improving the overall detection speed and accuracy of the YOLO-R algorithm. Feature entropy is introduced to the attention channel to restrict the threshold value and obtain the global information of the occlusion target. The global information obtained is fused with a bidirectional feature pyramid layer to enhance the robustness of the features. This method could accurately and quickly detect the occluded commodities and the detection accuracy has been greatly improved. Experiments show that the improved YOLO-R model can improve the accuracy and speed of commodity detection, and can achieve good results in objective evaluation. The average accuracy of commodity detection on the self-made product dataset is up to 97.80%, and the detection rate is 22.72F/s. Therefore, the method in this paper has high detection accuracy and fast detection speed. |
| first_indexed | 2024-03-12T12:25:00Z |
| format | Article |
| id | doaj.art-b68ec1e1b94749d085adafb269990c38 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T12:25:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b68ec1e1b94749d085adafb269990c382023-08-29T23:00:43ZengIEEEIEEE Access2169-35362023-01-0111905959061210.1109/ACCESS.2023.330695110225039Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function GuidanceAn Xie0Kai Xie1https://orcid.org/0000-0002-2511-5942Hao-Nan Dong2Jian-Biao He3https://orcid.org/0000-0001-5601-3335School of Electronic and Information, Yangtze University, Jingzhou, ChinaSchool of Electronic and Information, Yangtze University, Jingzhou, ChinaSchool of Electronic and Information, Yangtze University, Jingzhou, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaAlthough traditional convolutional neural networks (CNN) have been significantly improved for target detection, they cannot be completely applied to objects with occlusions in commodity detection. Therefore, we propose a target detection method based on an improved YOLOv5 model and an improved attention mechanism algorithm is proposed to solve the commodity occlusion problem. This method improves the traditional YOLO deep convolution network, features a more detailed BiFPN layer, and performs lightweight two-way feature fusion, where the multidimensional features of the commodities are convolved and fused, thus improving the overall detection speed and accuracy of the YOLO-R algorithm. Feature entropy is introduced to the attention channel to restrict the threshold value and obtain the global information of the occlusion target. The global information obtained is fused with a bidirectional feature pyramid layer to enhance the robustness of the features. This method could accurately and quickly detect the occluded commodities and the detection accuracy has been greatly improved. Experiments show that the improved YOLO-R model can improve the accuracy and speed of commodity detection, and can achieve good results in objective evaluation. The average accuracy of commodity detection on the self-made product dataset is up to 97.80%, and the detection rate is 22.72F/s. Therefore, the method in this paper has high detection accuracy and fast detection speed.https://ieeexplore.ieee.org/document/10225039/Lightweight feature fusionattention channelfeature entropybidirectional feature |
| spellingShingle | An Xie Kai Xie Hao-Nan Dong Jian-Biao He Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance IEEE Access Lightweight feature fusion attention channel feature entropy bidirectional feature |
| title | Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance |
| title_full | Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance |
| title_fullStr | Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance |
| title_full_unstemmed | Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance |
| title_short | Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance |
| title_sort | detection of commodities based on multi feature fusion and attention screening by entropy function guidance |
| topic | Lightweight feature fusion attention channel feature entropy bidirectional feature |
| url | https://ieeexplore.ieee.org/document/10225039/ |
| work_keys_str_mv | AT anxie detectionofcommoditiesbasedonmultifeaturefusionandattentionscreeningbyentropyfunctionguidance AT kaixie detectionofcommoditiesbasedonmultifeaturefusionandattentionscreeningbyentropyfunctionguidance AT haonandong detectionofcommoditiesbasedonmultifeaturefusionandattentionscreeningbyentropyfunctionguidance AT jianbiaohe detectionofcommoditiesbasedonmultifeaturefusionandattentionscreeningbyentropyfunctionguidance |