# Microbial Colony Detection Based on Deep Learning
## 2 Related Work  
### Object Detection  
There have been multiple significant advancements in the field of object detection in recent years with the introduction of deep learning-based techniques [24]. The current deep learning-based object detection techniques can be divided into two categories: one-stage object detection algorithms and two-stage object detection algorithms. The one-stage architectures predict classes and generate bounding box locations directly through a single forward propagation process, e.g., You Only Look Once (YOLO) [25] models and Single Shot Multi-Box Detector (SSD) [26]. The two-stage models [27] cope with the target detection task in two main processes. It first generates proposals, and then classifies and fine-tunes these proposals to achieve target detection and localization.  
On the other hand, Region-based Convolutional Neural Networks (R-CNNs) [28] introduced the concept of using region proposals for object detection. Proposed in 2013, R-CNN was one of the pioneering models that addressed the challenge of object detection through a two-stage process. It involves creating region proposals via selective search or a Region Proposal Network (RPN), and then using a CNN to classify and improve the bounding boxes for each proposal. Faster R-CNN [29] uses the Region of Interest (RoI) pooling layer to extract features from the full image in order to hasten training. Instead of employing the selective search technique to produce region proposals, an RPN is employed in the Faster R-CNN [30] architecture. This makes it possible to drastically cut down on the model's inference time. In addition to object detection, the Mask R-CNN [31] model, developed by [PERSON] et al. in 2017, adds a new branch for pixel-level semantic segmentation. Compared to Faster R-CNN, Cascade R-CNN [19] uses a cascade architecture that contains multi-stage classifiers and regressors to incrementally improve object detection performance by filtering false positives and refining predictions at each stage.  
The introduction of the Transformer model has revitalized various fields of machine learning, including object detection. The encoder component of the Transformer [32] breaks the image up into small patches for object detection and then encodes each patch into a fixed-dimensional representation. By adding heads after the encoder, the features of each image block are fused using a fully connected layer, resulting in an output feature map that acts as an overall image abstraction. This transformation enables the Transformer to replace traditional CNN-based feature extraction in target detection tasks, offering potential benefits for feature learning and performance improvement.  
Facebook AI research introduces an end-to-end Transformer (DETR) [33] architecture that changes the object identification issue into an ensemble prediction problem by skipping anchor frames and Non-Maximum Suppression (NMS) [34] operations in traditional detection. [PERSON] et al. [17] propose local self-attention mechanisms and stage features to deal with large-scale images, use the Swin operation to reduce complexity, and expand the sensory field through different stages. Furthermore, Transformer in Transformer (TNT) [35] proposes a way to nest another small Transformer block inside the Transformer encoder to learn the feature representation at a finer granularity.  
YOLOv8 is a state-of-the-art (SOTA) model released by the Ultralytics team in 2023, building upon the success of previous YOLO [36] releases to enhance both performance and flexibility. As a one-stage object detection model, YOLOv8 offers improved speed and accuracy in detection, and it supports multiple tasks, including object detection, instance segmentation, and image classification.