Advanced Methods for Image Processing and Object Detection Using AI

Abstract: This paper provides a detailed overview of key image processing techniques and object detection methods that are integral to modern computer vision systems. The study explores both traditional approaches, such as grayscale conversion, Gaussian filtering, and edge detection, as well as advanced deep learning methods including convolutional neural networks (CNNs), YOLO, and Faster R-CNN. Emphasis is placed on the role of preprocessing and feature extraction in enhancing the accuracy and efficiency of object detection systems. A comparative analysis is conducted to evaluate the performance of classical techniques versus CNN-based models in terms of detection accuracy, robustness, and computational complexity. Experimental results demonstrate that deep learning models significantly outperform traditional algorithms, particularly in complex and real-time environments. However, the study also acknowledges the advantages of classical methods in resource-constrained scenarios. The findings support the growing trend toward hybrid solutions that integrate conventional image processing with deep learning to develop scalable and adaptive vision-based systems.