An Efficient Self-Organized Detection System for Algae
Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/3/1609 |
| _version_ | 1827759442480857088 |
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| author | Xingrui Gong Chao Ma Beili Sun Junyi Zhang |
| author_facet | Xingrui Gong Chao Ma Beili Sun Junyi Zhang |
| author_sort | Xingrui Gong |
| collection | DOAJ |
| description | Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms. |
| first_indexed | 2024-03-11T09:25:30Z |
| format | Article |
| id | doaj.art-a5cb8f80e1534ced827477340910ede0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:25:30Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a5cb8f80e1534ced827477340910ede02023-11-16T18:03:33ZengMDPI AGSensors1424-82202023-02-01233160910.3390/s23031609An Efficient Self-Organized Detection System for AlgaeXingrui Gong0Chao Ma1Beili Sun2Junyi Zhang3School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, ChinaDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, ChinaJiangsu Metabio Science & Technology Co., Ltd., Wuxi 214028, ChinaJiangsu Wuxi Environmental Monitoring Center, Wuxi 214121, ChinaAlgal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms.https://www.mdpi.com/1424-8220/23/3/1609algal bloomsalgal detectionreal-time detectionself-organized detectionInternet of Things (IoT)algal detection dataset |
| spellingShingle | Xingrui Gong Chao Ma Beili Sun Junyi Zhang An Efficient Self-Organized Detection System for Algae Sensors algal blooms algal detection real-time detection self-organized detection Internet of Things (IoT) algal detection dataset |
| title | An Efficient Self-Organized Detection System for Algae |
| title_full | An Efficient Self-Organized Detection System for Algae |
| title_fullStr | An Efficient Self-Organized Detection System for Algae |
| title_full_unstemmed | An Efficient Self-Organized Detection System for Algae |
| title_short | An Efficient Self-Organized Detection System for Algae |
| title_sort | efficient self organized detection system for algae |
| topic | algal blooms algal detection real-time detection self-organized detection Internet of Things (IoT) algal detection dataset |
| url | https://www.mdpi.com/1424-8220/23/3/1609 |
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