Research on the impact of optimization algorithms on the accuracy of Yolov11 neural networks

Abstract

Visual inspection and positioning based on image detection results is a rapidly growing component of automation systems. Machine vision is increasingly used in production lines for various purposes. Improving recognition accuracy in such applications can be a difficult task, especially in conditions of possible limitations, one of which may be size and weight restrictions, which in turn limit the power of computer devices that implement image detection and recognition. A possible solution to this problem is to improve recognition accuracy by increasing the number of image variants in the dataset and fine-tuning the model's hyperparameters. This article investigates the effectiveness of hyperparameter tuning for the YOLO (You Only Look Once) image detection model, which can be further implemented in electromechanical systems for positioning moving objects in space.