Object detection is a fundamental task in computer vision that focuses on identifying objects and determining their exact locations within images or videos. With the rapid growth of digital visual data and increasing demand for automation, object detection has become a critical component in applications such as surveillance systems, autonomous vehicles, medical image analysis, robotics, and assistive technologies. Earlier object detection methods relied heavily on handcrafted features and traditional machine learning algorithms, which often struggled with complex backgrounds, scale variations, and real-time performance requirements.
Recent advancements in deep learning, particularly convolutional neural networks (CNNs) and transformer- based architectures, have significantly improved detection accuracy and robustness. Techniques such as R-CNN, Faster R-CNN, SSD, YOLO, and transformer- based models have enabled efficient end-to-end object detection with better handling of multi-scale and high- dimensional data. This paper presents a comprehensive study of object detection techniques, covering both traditional machine learning approaches and modern deep learning-based methods. The paper discusses their working principles, advantages, limitations, and application areas. The study aims to provide a clear understanding of the evolution of object detection techniques and highlight current challenges and future research directions in this domain.