Magnetic-Controlled Microrobot: Real-Time Detection and Tracking through Deep Learning Approaches

Abstract

As one of the most significant research topics in robotics, microrobots hold great promise in biomedicine for applications such as targeted diagnosis, targeted drug delivery, and minimally invasive treatment. This paper proposes an enhanced YOLOv5 (You Only Look Once version 5) microrobot detection and tracking system (MDTS), incorporating a visual tracking algorithm to elevate the precision of small-target detection and tracking. The improved YOLOv5 network structure is used to take magnetic bodies with sizes of 3 mm and 1 mm and a magnetic microrobot with a length of 2 mm as the pretraining targets, and the training weight model is used to obtain the position information and motion information of the microrobot in real time. The experimental results show that the accuracy of the improved network model for magnetic bodies with a size of 3 mm is 95.81%, representing an increase of 2.1%; for magnetic bodies with a size of 1 mm, the accuracy is 91.03%, representing an increase of 1.33%; and for microrobots with a length of 2 mm, the accuracy is 91.7%, representing an increase of 1.5%. The combination of the improved YOLOv5 network model and the vision algorithm can effectively realize the real-time detection and tracking of magnetically controlled microrobots. Finally, 2D and 3D detection and tracking experiments relating to microrobots are designed to verify the robustness and effectiveness of the system, which provides strong support for the operation and control of microrobots in an in vivo environment.