A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage
Recent advances in fish transportation technologies and deep machine learning-based fish classification have created an opportunity for real-time, autonomous fish sorting through a selective passage mechanism. This research presents a case study of a novel application that utilizes deep machine lear...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1022 |
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| author | Jonathan Gregory Scott M. Miehls Jesse L. Eickholt Daniel P. Zielinski |
| author_facet | Jonathan Gregory Scott M. Miehls Jesse L. Eickholt Daniel P. Zielinski |
| author_sort | Jonathan Gregory |
| collection | DOAJ |
| description | Recent advances in fish transportation technologies and deep machine learning-based fish classification have created an opportunity for real-time, autonomous fish sorting through a selective passage mechanism. This research presents a case study of a novel application that utilizes deep machine learning to detect partially dewatered fish exiting an Archimedes Screw Fish Lift (ASFL). A MobileNet SSD model was trained on images of partially dewatered fish volitionally passing through an ASFL. Then, this model was integrated with a network video recorder to monitor video from the ASFL. Additional models were also trained using images from a similar fish scanning device to test the feasibility of this approach for fish classification. Open source software and edge computing design principles were employed to ensure that the system is capable of fast data processing. The findings from this research demonstrate that such a system integrated with an ASFL can support real-time fish detection. This research contributes to the goal of automated data collection in a selective fish passage system and presents a viable path towards realizing optical fish sorting. |
| format | Article |
| id | doaj-art-ca9fb4fd5e49462ebb30599530bde753 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ca9fb4fd5e49462ebb30599530bde7532025-08-20T02:44:29ZengMDPI AGSensors1424-82202025-02-01254102210.3390/s25041022A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish PassageJonathan Gregory0Scott M. Miehls1Jesse L. Eickholt2Daniel P. Zielinski3Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USAU.S. Geological Survey, Great Lakes Science Center, Hammond Bay Biological Station, Millersburg, MI 49759, USADepartment of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USAGreat Lakes Fishery Commission, Ann Arbor, MI 48105, USARecent advances in fish transportation technologies and deep machine learning-based fish classification have created an opportunity for real-time, autonomous fish sorting through a selective passage mechanism. This research presents a case study of a novel application that utilizes deep machine learning to detect partially dewatered fish exiting an Archimedes Screw Fish Lift (ASFL). A MobileNet SSD model was trained on images of partially dewatered fish volitionally passing through an ASFL. Then, this model was integrated with a network video recorder to monitor video from the ASFL. Additional models were also trained using images from a similar fish scanning device to test the feasibility of this approach for fish classification. Open source software and edge computing design principles were employed to ensure that the system is capable of fast data processing. The findings from this research demonstrate that such a system integrated with an ASFL can support real-time fish detection. This research contributes to the goal of automated data collection in a selective fish passage system and presents a viable path towards realizing optical fish sorting.https://www.mdpi.com/1424-8220/25/4/1022selective fish passageobject detectionedge computingMobileNet SSDreal-time detection |
| spellingShingle | Jonathan Gregory Scott M. Miehls Jesse L. Eickholt Daniel P. Zielinski A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage Sensors selective fish passage object detection edge computing MobileNet SSD real-time detection |
| title | A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage |
| title_full | A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage |
| title_fullStr | A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage |
| title_full_unstemmed | A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage |
| title_short | A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage |
| title_sort | real time fish detection system for partially dewatered fish to support selective fish passage |
| topic | selective fish passage object detection edge computing MobileNet SSD real-time detection |
| url | https://www.mdpi.com/1424-8220/25/4/1022 |
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