Lightweight target detection for large-field ddPCR images based on improved YOLOv5

The dataset and code used in this study are crucial for advancing the accurate detection of positive microchambers in large-field ddPCR imaging. The provided dataset includes annotated ddPCR images in YOLO format, stored in the `ddpcr320/` folder. The codebase features the improved YOLOv5 model, integrating BiFPN, GhostConv, C3Ghost modules, SimAM attention mechanism, and network pruning, among other custom modifications. The `train.py` and `detect.py` scripts handle training and detection tasks, while `dataset.ipynb` demonstrates the dataset creation and splitting processes, as well as dataset processing and augmentation. The graphical user interface, developed using PyQt5 and implemented in `main_win.py`, facilitates image processing and result analysis for users. The project structure, `ddpcr_yolov5`, is systematically organized, with detailed instructions provided in the README.md file.本研究使用的数据集与代码，对于推进大视野液滴数字PCR（ddPCR）成像中阳性微腔的精准检测至关重要。所提供的数据集包含标注为YOLO（You Only Look Once）格式的ddPCR图像，存储于`ddpcr320/`文件夹内。该代码库搭载了改进版YOLOv5模型，集成了双向特征金字塔网络（BiFPN）、Ghost卷积（GhostConv）、C3Ghost模块、SimAM注意力机制（SimAM）以及网络剪枝等多项自定义改进。其中，`train.py`与`detect.py`脚本分别负责训练与检测任务，`dataset.ipynb`则演示了数据集的创建、划分、处理与增强流程。本项目采用PyQt5开发的图形用户界面（GUI）已在`main_win.py`中实现，可协助用户完成图像处理与结果分析工作。本项目的目录结构为`ddpcr_yolov5`，整体组织井然有序，详细使用说明已收录于README.md文件中。