Edge-Guided Lighting Adaptation: Real-Time Detection of Transparent Objects for Cell Culture Robot

Abstract

In robot-assisted cell culture tasks, fluctuations in lighting conditions can result in blurred boundaries, intensified reflections, and pronounced refractions of transparent objects. These optical phenomena collectively escalate the complexity of image processing and target recognition. To address these challenges, this paper takes a dual-strategy approach. Firstly, it utilizes the Unity platform to construct a synthetic dataset (STTO-9k) containing 9,000 images of six types of transparent objects, providing abundant training samples for the detection and recognition of transparent objects. Secondly, it proposes an improved YOLOv8 visual detection algorithm (YOLO-Edge-Guided Lighting Adaptation, YL-EGLA). The algorithm realizes feature fusion by dynamically extracting the high-dimensional features of the input through the self-attention mechanism combined with the enhanced edge features extracted by the edge detection operator, and is equipped with adaptive image enhancement module to ensure stable detection under different lighting conditions. Algorithm comparison results demonstrate that the YL-EGLA can be fully trained on the synthetic dataset and directly applied to real-world scenarios without additional fine-tuning. Furthermore, physical experiments further validate the efficiency and practicality of this algorithm in transparent object manipulation, fully showcasing its significant value in practical applications.