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Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution. Secondly, a phase contrast image quality optimisation algorithm based on fused variance is proposed, which can effectively improve the standard deviation, entropy, and detection performance. Thirdly, a lightweight YOLOv8n\u2010SimAM model is designed, which introduces a SimAM attention module to suppress the complex background interference and enhance attentions to the target objects. The lightweight of the network is realised using a detection head based on multiscale information fusion convolutional module. In addition, a new loss function IW\u2010IoU is proposed to improve the generalisation ability and overall performance. Comparative and ablative experiments are conducted, demonstrating the great application potential for rapid and accurate detection of microbial targets. Compared to the baseline model, the proposed method improves the detection accuracy by 12.35% and hastens the running speed by 37.9 frames per second while evidently reducing the model size.<\/jats:p>","DOI":"10.1111\/jmi.13385","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T15:00:53Z","timestamp":1737644453000},"page":"58-73","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning"],"prefix":"10.1111","volume":"298","author":[{"given":"Dan","family":"Liang","sequence":"first","affiliation":[{"name":"Ningbo Key Laboratory of Micro\u2010Nano Motion and Intelligent Control Ningbo University  Ningbo PR China"},{"name":"Part Rolling Key Laboratory of Zhejiang Province Ningbo University  Ningbo PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuming","family":"Yao","sequence":"additional","affiliation":[{"name":"Ningbo Key Laboratory of Micro\u2010Nano Motion and Intelligent Control Ningbo University  Ningbo PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjie","family":"Ye","sequence":"additional","affiliation":[{"name":"Ningbo Key Laboratory of Micro\u2010Nano Motion and Intelligent Control Ningbo University  Ningbo PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinze","family":"Luo","sequence":"additional","affiliation":[{"name":"Ningbo Key Laboratory of Micro\u2010Nano Motion and Intelligent Control Ningbo University  Ningbo PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Chu","sequence":"additional","affiliation":[{"name":"Ningbo Key Laboratory of Micro\u2010Nano Motion and Intelligent Control Ningbo University  Ningbo PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"87","article-title":"The role and significance of extracellular polymers in activated sludge","volume":"5","author":"Raszka A.","year":"2024","journal-title":"John Wiley & Sons"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/jmi.13249"},{"key":"e_1_2_9_4_1","first-page":"437","article-title":"Filamentous microorganisms in the bulked activated sludges","volume":"95","author":"Kalisz L.","year":"2016","journal-title":"Przemysl Chemiczny"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2780249"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jwpe.2024.105692"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2023.1200994"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4621403"},{"key":"e_1_2_9_9_1","doi-asserted-by":"crossref","unstructured":"Hung J. &Carpenter A.(2017).Applying faster R\u2010CNN for object detection on malaria images. 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