Object Detection and Semantic Segmentation Using Deep Learning Techniques

Abstract

Object detection and semantic segmentation are two fundamental tasks in computer vision that have been significantly advanced by deep learning techniques. Object detection involves identifying and localizing objects within an image, while semantic segmentation assigns a class label to each pixel, providing a detailed understanding of image content. This paper explores the methodologies and architectures that underpin modern object detection and semantic segmentation systems, including convolutional neural networks (CNNs), region-based methods, and transformer-based models. It discusses key challenges such as computational efficiency, handling occlusions, and improving model generalization. Furthermore, we highlight real-world applications across industries, including autonomous driving, medical imaging, and surveillance. By examining recent advancements and best practices, this paper provides insights into the future directions of deep learning for object detection and semantic segmentation.