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

翻译：入侵性信号螯虾对生态系统具有破坏性影响。它们传播真菌型螯虾瘟疫病（Aphanomyces astaci），该疾病对英国唯一的本土螯虾物种——白爪螯虾具有致命性。入侵性信号螯虾大量掘穴，导致栖息地破坏、河岸侵蚀及水质恶化，同时与本土物种竞争资源，致使本土种群数量下降。此外，污染加剧了白爪螯虾的生存脆弱性，其种群数量已下降超过90%。为保护水生生态系统，必须应对入侵物种和污染带来的挑战。本文介绍了用于检测螯虾与塑料的认知边缘设备（CED）计算平台，并公开了两个标注有螯虾序列和水生塑料碎片序列的水下数据集。研究训练并评估了四种You Only Look Once（YOLO）变体模型用于螯虾与塑料目标检测。其中YOLOv5s取得了最高的检测准确率，其mAP@0.5达到0.90，并获得了最优的精确度。