Detection of Liquid Retention on Pipette Tips in High-Throughput Liquid Handling Workstations Based on Improved YOLOv8 Algorithm with Attention Mechanism

Abstract

High-throughput liquid handling workstations are required to process large numbers of test samples in the fields of life sciences and medicine. Liquid retention and droplets hanging in the pipette tips can lead to cross-contamination of samples and reagents and inaccurate experimental results. Traditional methods for detecting liquid retention have low precision and poor real-time performance. This paper proposes an improved YOLOv8 (You Only Look Once version 8) object detection algorithm to address the challenges posed by different liquid sizes and colors, complex situation of test tube racks and multiple samples in the background, and poor global image structure understanding in pipette tip liquid retention detection. A global context (GC) attention mechanism module is introduced into the backbone network and the cross-stage partial feature fusion (C2f) module to better focus on target features. To enhance the ability to effectively combine and process different types of data inputs and background information, a Large Kernel Selection (LKS) module is also introduced into the backbone network. Additionally, the neck network is redesigned to incorporate the Simple Attention (SimAM) mechanism module, generating attention weights and improving overall performance. We evaluated the algorithm using a self-built dataset of pipette tips. Compared to the original YOLOv8 model, the improved algorithm increased mAP@0.5 (mean average precision), F1 score, and precision by 1.7%, 2%, and 1.7%, respectively. The improved YOLOv8 algorithm can enhance the detection capability of liquid-retaining pipette tips, and prevent cross-contamination from affecting the results of sample solution experiments. It provides a detection basis for subsequent automatic processing of solution for liquid retention.