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

TL;DR

This paper reviews the evolution of microorganism detection methods from traditional image processing to deep learning, highlighting recent advances, potential future techniques like visual transformers, and discussing ongoing challenges and development directions.

Contribution

It provides a comprehensive survey of 142 papers, analyzing the progression of detection methods and introducing potential future approaches such as visual transformers.

Findings

Traditional methods are being replaced by deep learning techniques.

Deep learning significantly improves detection accuracy and efficiency.

Future trends include the integration of visual transformers.

Abstract

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.