Research and Optimization of Methods for Detecting Objects in Images

This work has conducted research and optimization of object detection methods in images using modern deep learning approaches. The work has conducted a theoretical analysis of the object detection problem, considered the role of computer vision in the modern information environment, and analyzed domestic and foreign scientific and technical sources. An analysis of existing neural network architectures, in particular YOLOv8, Faster R-CNN, and DETR, was conducted, with the determination of their advantages, disadvantages, and areas of effective application. The selected models were optimized by selecting hyperparameters, improving learning processes, and increasing the balance between accuracy and speed. Experimental implementation and comparison of models were conducted, which allowed assessing the impact of the applied optimization methods on the efficiency of detection systems