Multiscale apple recognition method based on improved CenterNet
Traditional apple-picking robots are unable to detect apples in real-time in complex environments. In order to improve detection efficiency, a fast CenterNet apple recognition method for multiple apple targets in dense scenes is proposed. This method can quickly and accurately identify multiple appl...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024050667 |
| _version_ | 1797214900258340864 |
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| author | Han Zhou |
| author_facet | Han Zhou |
| author_sort | Han Zhou |
| collection | DOAJ |
| description | Traditional apple-picking robots are unable to detect apples in real-time in complex environments. In order to improve detection efficiency, a fast CenterNet apple recognition method for multiple apple targets in dense scenes is proposed. This method can quickly and accurately identify multiple apple targets in dense scenes. The backbone network mainly consists of resnet-44 fully convolutional network, region of interest network (RPN), and region of interest (ROI). The experimental results show that the improved YoloV5 network model has a higher recognition accuracy of 94.1% and 95.8% for apple in the night environment, which improves the recognition accuracy of the occluded features and the features in the dark light, and the model is more robust in the actual data set. |
| first_indexed | 2024-04-24T11:21:31Z |
| format | Article |
| id | doaj.art-81126beb73584eb9b7d27e748528c63d |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T11:21:31Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-81126beb73584eb9b7d27e748528c63d2024-04-11T04:41:36ZengElsevierHeliyon2405-84402024-04-01107e29035Multiscale apple recognition method based on improved CenterNetHan Zhou0College of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou, 571126, Hainan, ChinaTraditional apple-picking robots are unable to detect apples in real-time in complex environments. In order to improve detection efficiency, a fast CenterNet apple recognition method for multiple apple targets in dense scenes is proposed. This method can quickly and accurately identify multiple apple targets in dense scenes. The backbone network mainly consists of resnet-44 fully convolutional network, region of interest network (RPN), and region of interest (ROI). The experimental results show that the improved YoloV5 network model has a higher recognition accuracy of 94.1% and 95.8% for apple in the night environment, which improves the recognition accuracy of the occluded features and the features in the dark light, and the model is more robust in the actual data set.http://www.sciencedirect.com/science/article/pii/S2405844024050667Apple recognitionImprove YoloV5ROIResnet-44 |
| spellingShingle | Han Zhou Multiscale apple recognition method based on improved CenterNet Heliyon Apple recognition Improve YoloV5 ROI Resnet-44 |
| title | Multiscale apple recognition method based on improved CenterNet |
| title_full | Multiscale apple recognition method based on improved CenterNet |
| title_fullStr | Multiscale apple recognition method based on improved CenterNet |
| title_full_unstemmed | Multiscale apple recognition method based on improved CenterNet |
| title_short | Multiscale apple recognition method based on improved CenterNet |
| title_sort | multiscale apple recognition method based on improved centernet |
| topic | Apple recognition Improve YoloV5 ROI Resnet-44 |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024050667 |
| work_keys_str_mv | AT hanzhou multiscaleapplerecognitionmethodbasedonimprovedcenternet |