HYBRID IMAGE PROCESSING AND DEEP LEARNING TECHNIQUES FOR OBJECT DETECTION

Abstract

Automated object detection plays a vital role in modern image-based applications such as surveillance, autonomous systems, and industrial inspection. Traditional manual detection methods are time-consuming and prone to human error. Digital image processing techniques provide efficient and accurate solutions for identifying objects within images. This research presents a structured approach to automated object detection using image preprocessing, segmentation, feature extraction, and classification techniques. The proposed system enhances detection accuracy while maintaining computational efficiency. Experimental evaluation is performed on standard image datasets. Results demonstrate reliable detection performance under varying conditions. The study emphasizes the effectiveness of digital image processing in object detection applications.