CONFERENCE / ICCAIS-2026

Object detection, which serves as a fundamental element of computer vision, enables users to detect and position objects in pictures. This capability plays an essential role in various applications that include autonomous vehicles, security systems, healthcare solutions, and retail analytics. The article investigates all existing object detection techniques, including their recent advancements that use Convolutional Neural Networks (CNNs) for basic methods and various advanced methods that include few-shot learning, zero-shot learning, synthetic data creation, and domain adaptation. Initially, object detection relied on large annotated datasets and sophisticated model architectures to achieve accurate results. However, recent advancements have introduced methods that reduce data dependency, such as synthetic image generation and text-to-image synthesis, which allow for customizable datasets tailored to unique use cases. This development has been paralleled by the rise of domain adaptation techniques, which enable models to generalize better across diverse conditions and environments. The article investigates how ensemble techniques can improve detection accuracy and system resilience while exploring how Generative Adversarial Networks (GANs) create authentic synthetic data. The article presents few-shot and zero-shot learning methods which enable identification of new classes using only a few labeled examples, which proves valuable in settings that constantly introduce fresh object categories. This article aims to provide an in-depth overview of these cutting-edge techniques, discussing their respective strengths and applications, as well as the limitations and ethical challenges posed by object detection in real-world deployments. The research establishes a complete understanding of current object detection technology during its present phase while including information about future advancements in the field.