{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:54:51Z","timestamp":1787982891607,"version":"build-2784847793"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>Environmental microorganism (EM) detection from microscopy images supports scalable water-environment monitoring, but fair comparison of detection algorithms is hindered by inconsistent experimental protocols and limited benchmark reporting. We construct a reproducible EM object detection benchmark on the EMDS-7 dataset and, using a fixed split, unified input normalization, and from-scratch training without external pretraining, evaluate 25 representative detectors spanning two-stage proposal-based methods, one-stage dense detectors, keypoint-based formulations, and Transformer-based end-to-end approaches. Performance is measured with COCO-style mAP over IoU 0.50:0.05:0.95, complemented by AP across IoU thresholds, recall-based analysis, and backbone comparisons across ResNet-18\/50\/101 where supported. Two-stage detectors achieve the strongest overall accuracy on EMDS-7, with Faster R-CNN obtaining the best mAP (64.0%) and Cascade R-CNN close behind (63.9%), while modern one-stage detectors substantially narrow the gap, exemplified by RTMDet-X (60.9%). AP consistently decreases as IoU becomes stricter, indicating that precise localization is a key bottleneck in EM microscopy; backbone scaling does not yield uniform gains under from-scratch training, and recall analysis reveals distinct operating characteristics across paradigms, including high-recall but lower-precision tendencies for some methods. These results suggest that EMDS-7 favors detectors with robust localization and controlled false positives under cluttered microscopic backgrounds, and that future progress should emphasize high-IoU localization for small instances, boundary-aware learning, and false-positive suppression rather than relying solely on deeper backbones; the proposed benchmark provides reproducible baselines and diagnostic evidence to guide subsequent EM detection research.<\/jats:p>","DOI":"10.3389\/fmicb.2026.1821911","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T05:48:10Z","timestamp":1776059290000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A benchmark for environmental microorganism object detection on EMDS-7"],"prefix":"10.3389","volume":"17","author":[{"given":"Jia","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Hunan First Normal University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanjuan","family":"Guo","sequence":"additional","affiliation":[{"name":"The Second Clinical Hospital of Lanzhou University","place":["Lanzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hunan First Normal University","place":["Changsha, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"295","DOI":"10.3390\/mi8100295","article-title":"Microrobotic platform for single motile microorganism investigation","volume":"8","author":"Ahmad","year":"2017","journal-title":"Micromachines"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1632344","DOI":"10.3389\/frai.2025.1632344","article-title":"Enhancing ai microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability","volume":"8","author":"Bhattacharya","year":"2025","journal-title":"Front. 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