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

Abstract

The large-field rapid nucleic acid concentration measurement system is capable of achieving one-time gene chip imaging with high resolution. However, it encounters challenges in the precise detection of positive microchambers, which is caused by factors such as reagent residue, uneven lighting, and environmental noise. Herein we proposed an improved, lightweight algorithm based on You Only Look Once (YOLOv5) for detecting the positive microchambers. We determined appropriate detection scales based on the target size distribution and utilized the bidirectional feature pyramid network (BiFPN) for efficient multi-scale feature fusion. To reduce model size without sacrificing performance, GhostConv, C3Ghost, and a simple, parameter-free attention module (SimAM) were integrated into the network, followed by network pruning. The improved YOLOv5 model was trained on a self-built dataset, and employed a partitioned fusion prediction strategy to detect large-field ddPCR images by self-developed software. In contrast to single-stage lightweight object detection algorithms, our model features a mere 1.5MB size while achieving 99.5% precision, 99.5% recall, and a 78.1% mAP(0.5 : 0.95), significantly reducing the system's demand for computing resources without compromising efficiency and accuracy.