Cognitive Edge Device (CED) for Real-Time Environmental Monitoring in Aquatic Ecosystems
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911428921786368 |
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| author | Monari, Dennis Tash, Farhad Fassihi Bird, Jordan J. Lotfi, Ahmad Ihianle, Isibor Kennedy Yahaya, Salisu Wada Ihianle, Isibor Kennedy Hasan, Md Mahmudul Sousa, Pedro Machado, Pedro |
| author_facet | Monari, Dennis Tash, Farhad Fassihi Bird, Jordan J. Lotfi, Ahmad Ihianle, Isibor Kennedy Yahaya, Salisu Wada Ihianle, Isibor Kennedy Hasan, Md Mahmudul Sousa, Pedro Machado, Pedro |
| contents | Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for resources leading to declines in native populations. Moreover, pollution exacerbates the vulnerability of White-clawed crayfish, with their populations declining by over 90%. To safeguard aquatic ecosystems, it is imperative to address the challenges posed by invasive species and pollution in aquatic ecosystem's. This article introduces the Cognitive Edge Device (CED) computing platform for the detection of crayfish and plastic. It also presents two publicly available underwater datasets, annotated with sequences of crayfish and aquatic plastic debris. Four You Only Look Once (YOLO) variants were trained and evaluated for crayfish and plastic object detection. YOLOv5s achieved the highest detection accuracy, with an mAP@0.5 of 0.90, and achieved the best precision |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06157 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Cognitive Edge Device (CED) for Real-Time Environmental Monitoring in Aquatic Ecosystems Monari, Dennis Tash, Farhad Fassihi Bird, Jordan J. Lotfi, Ahmad Ihianle, Isibor Kennedy Yahaya, Salisu Wada Ihianle, Isibor Kennedy Hasan, Md Mahmudul Sousa, Pedro Machado, Pedro Computer Vision and Pattern Recognition Artificial Intelligence Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for resources leading to declines in native populations. Moreover, pollution exacerbates the vulnerability of White-clawed crayfish, with their populations declining by over 90%. To safeguard aquatic ecosystems, it is imperative to address the challenges posed by invasive species and pollution in aquatic ecosystem's. This article introduces the Cognitive Edge Device (CED) computing platform for the detection of crayfish and plastic. It also presents two publicly available underwater datasets, annotated with sequences of crayfish and aquatic plastic debris. Four You Only Look Once (YOLO) variants were trained and evaluated for crayfish and plastic object detection. YOLOv5s achieved the highest detection accuracy, with an mAP@0.5 of 0.90, and achieved the best precision |
| title | Cognitive Edge Device (CED) for Real-Time Environmental Monitoring in Aquatic Ecosystems |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.06157 |