  In recent years, deep learning technology has developed rapidly, and the
application of deep neural networks in the medical image processing field has
become the focus of the spotlight. This paper aims to achieve needle position
detection in medical retinal surgery by adopting the target detection algorithm
based on YOLOv5 as the basic deep neural network model. The state-of-the-art
needle detection approaches for medical surgery mainly focus on needle
structure segmentation. Instead of the needle segmentation, the proposed method
in this paper contains the angle examination during the needle detection
process. This approach also adopts a novel classification method based on the
different positions of the needle to improve the model. The experiments
demonstrate that the proposed network can accurately detect the needle position
and measure the needle angle. The performance test of the proposed method
achieves 4.80 for the average Euclidean distance between the detected tip
position and the actual tip position. It also obtains an average error of 0.85
degrees for the tip angle across all test sets.
