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.