mlsanityData to 14 Oct 2026

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

arXiv 2105.03148 ↗First posted 7 May 2021cs.CV cs.AIcited by 12

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Abstract (arXiv)

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 51 of its 246 references that are in the map. First posted on arXiv 7 May 2021.

Builds on

51 of 246 references.

References · 51

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  6. 2020End-to-End Object Detection with Transformerscited by 7,414
  7. 2017Mask R-CNNcited by 7,277
  8. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  10. 2017Squeeze-and-Excitation Networkscited by 4,789
  11. 2014Going Deeper with Convolutionscited by 4,241
  12. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
38 more
  1. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  2. 2015Fast R-CNNcited by 2,933
  3. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  4. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  5. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  6. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  7. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  8. 2015Spatial Transformer Networkscited by 1,429
  9. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
  10. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  11. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 579
  12. 2019Object Detection in 20 Years: A Surveycited by 531
  13. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  14. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  15. 2020DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolutioncited by 222
  16. 20203D Object Detection with Pointformercited by 194
  17. 2017Spatial As Deep: Spatial CNN for Traffic Scene Understandingcited by 187
  18. 2020Rethinking Transformer-based Set Prediction for Object Detectioncited by 156
  19. 2017Dataset Augmentation in Feature Spacecited by 124
  20. 2019PolyTransform: Deep Polygon Transformer for Instance Segmentationcited by 92
  21. 2020Toward Transformer-Based Object Detectioncited by 84
  22. 2016A Survey on Object Detection in Optical Remote Sensing Imagescited by 78
  23. 2020A Universal Representation Transformer Layer for Few-Shot Image Classificationcited by 65
  24. 2021A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approachescited by 44
  25. 2017FSSD: Feature Fusion Single Shot Multibox Detectorcited by 43
  26. 2020A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networkscited by 25
  27. 2021DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniquescited by 23
  28. 2021A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approachescited by 20
  29. 2020Gastric histopathology image segmentation using a hierarchical conditional random fieldcited by 16
  30. 2018Applying Faster R-CNN for Object Detection on Malaria Imagescited by 15
  31. 2020A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysiscited by 13
  32. 2021A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysiscited by 12
  33. 2020A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentationcited by 9
  34. 2021EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Taskscited by 7
  35. 2020A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and Visual Featurescited by 6
  36. 2020Multi-Target Deep Learning for Algal Detection and Classificationcited by 4
  37. 2017A Guided Spatial Transformer Network for Histology Cell Differentiationcited by 2
  38. 2016An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detectioncited by 1

50 most cited of 51 All 51 (JSON)

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Cited by year

Citing papers per year: 2021: 1; 2022: 5; 2023: 4; 2024: 2.

12 papers.

  1. 2022EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Taskscited by 15
  2. 2022EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluationcited by 3
  3. 2021EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluationcited by 2
  4. 2022A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformercited by 2
  5. 2023AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluationcited by 2
  6. 2023PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhagecited by 2
  7. 2022An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism imagescited by 1
  8. 2023ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regionscited by 1
  9. 2024FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagerycited by 1
  10. 2022A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortificationcited by 0
  11. 2023High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detectioncited by 0
  12. 2024Deep Learning-based Detection of Bacterial Swarm Motion Using a Single Imagecited by 0