Deep Learning-Based Oyster Packaging System
With the deepening understanding of the nutritional value of oysters by consumers, oysters as high-quality seafood are gradually entering the market. Raw edible oyster production lines mainly rely on manual sorting and packaging, which hinders the improvement of oyster packaging efficiency and quali...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/24/13105 |
| _version_ | 1797382200156487680 |
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| author | Ruihua Zhang Xujun Chen Zhengzhong Wan Meng Wang Xinqing Xiao |
| author_facet | Ruihua Zhang Xujun Chen Zhengzhong Wan Meng Wang Xinqing Xiao |
| author_sort | Ruihua Zhang |
| collection | DOAJ |
| description | With the deepening understanding of the nutritional value of oysters by consumers, oysters as high-quality seafood are gradually entering the market. Raw edible oyster production lines mainly rely on manual sorting and packaging, which hinders the improvement of oyster packaging efficiency and quality, and it is easy to cause secondary oyster pollution and cross-contamination, which results in the waste of oysters. To enhance the production efficiency, technical level, and hygiene safety of the raw aquatic products production line, this study proposes and constructs a deep learning-based oyster packaging system. The system achieves intelligence and automation of the oyster packaging production line by integrating the deep learning algorithm, machine vision technology, and mechanical arm control technology. The oyster visual perception model is established by deep learning object detection techniques to realize fast and real-time detection of oysters. Using a simple online real-time tracking (SORT) algorithm, the grasping position of the oyster can be predicted, which enables dynamic grasping. Utilizing mechanical arm control technology, an automatic oyster packaging production line was designed and constructed to realize the automated grasping and packaging of raw edible oysters, which improves the efficiency and quality of oyster packaging. System tests showed that the absolute error in oyster pose estimation was less than 7 mm, which allowed the mechanical claw to consistently grasp and transport oysters. The static grasping and packing of a single oyster took about 7.8 s, and the success rate of grasping was 94.44%. The success rate of grasping under different transportation speeds was above 68%. |
| first_indexed | 2024-03-08T21:02:00Z |
| format | Article |
| id | doaj.art-cf29fafdfc524c0cb1600057df52fbb3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:02:00Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cf29fafdfc524c0cb1600057df52fbb32023-12-22T13:51:27ZengMDPI AGApplied Sciences2076-34172023-12-0113241310510.3390/app132413105Deep Learning-Based Oyster Packaging SystemRuihua Zhang0Xujun Chen1Zhengzhong Wan2Meng Wang3Xinqing Xiao4College of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaWith the deepening understanding of the nutritional value of oysters by consumers, oysters as high-quality seafood are gradually entering the market. Raw edible oyster production lines mainly rely on manual sorting and packaging, which hinders the improvement of oyster packaging efficiency and quality, and it is easy to cause secondary oyster pollution and cross-contamination, which results in the waste of oysters. To enhance the production efficiency, technical level, and hygiene safety of the raw aquatic products production line, this study proposes and constructs a deep learning-based oyster packaging system. The system achieves intelligence and automation of the oyster packaging production line by integrating the deep learning algorithm, machine vision technology, and mechanical arm control technology. The oyster visual perception model is established by deep learning object detection techniques to realize fast and real-time detection of oysters. Using a simple online real-time tracking (SORT) algorithm, the grasping position of the oyster can be predicted, which enables dynamic grasping. Utilizing mechanical arm control technology, an automatic oyster packaging production line was designed and constructed to realize the automated grasping and packaging of raw edible oysters, which improves the efficiency and quality of oyster packaging. System tests showed that the absolute error in oyster pose estimation was less than 7 mm, which allowed the mechanical claw to consistently grasp and transport oysters. The static grasping and packing of a single oyster took about 7.8 s, and the success rate of grasping was 94.44%. The success rate of grasping under different transportation speeds was above 68%.https://www.mdpi.com/2076-3417/13/24/13105oysterdeep learningmachine visiongraspingpackaging |
| spellingShingle | Ruihua Zhang Xujun Chen Zhengzhong Wan Meng Wang Xinqing Xiao Deep Learning-Based Oyster Packaging System Applied Sciences oyster deep learning machine vision grasping packaging |
| title | Deep Learning-Based Oyster Packaging System |
| title_full | Deep Learning-Based Oyster Packaging System |
| title_fullStr | Deep Learning-Based Oyster Packaging System |
| title_full_unstemmed | Deep Learning-Based Oyster Packaging System |
| title_short | Deep Learning-Based Oyster Packaging System |
| title_sort | deep learning based oyster packaging system |
| topic | oyster deep learning machine vision grasping packaging |
| url | https://www.mdpi.com/2076-3417/13/24/13105 |
| work_keys_str_mv | AT ruihuazhang deeplearningbasedoysterpackagingsystem AT xujunchen deeplearningbasedoysterpackagingsystem AT zhengzhongwan deeplearningbasedoysterpackagingsystem AT mengwang deeplearningbasedoysterpackagingsystem AT xinqingxiao deeplearningbasedoysterpackagingsystem |