COMPUTER VISION FOR INTELLIGENT OBJECT DETECTION: MODELS, APPLICATIONS, AND CHALLENGES

Computer vision is an important field of artificial intelligence that enables computer systems to interpret and analyse visual information from images and videos. Among its major tasks, intelligent object detection focuses on identifying objects, assigning class labels, and determining their locations within a visual scene. Recent advances in deep learning have substantially improved object-detection capabilities, particularly through convolutional neural networks (CNNs), single-stage detectors, region-based detection frameworks, and transformer-based architectures. Models such as Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Detection Transformer (DETR) represent different approaches to balancing detection accuracy, computational efficiency, and model complexity. These techniques support applications in autonomous transportation, healthcare, agriculture, industrial inspection, surveillance, robotics, and smart-city infrastructure. Nevertheless, object detection remains challenging because of variations in lighting, object scale, occlusion, background complexity, limited labelled data, computational constraints, and potential bias in training datasets. This paper reviews the fundamental principles of intelligent object detection, discusses major model architectures and their applications, and examines the technical and ethical challenges associated with real-world deployment. It also identifies research directions involving efficient models, improved small-object detection, domain adaptation, explainability, privacy protection, and responsible artificial intelligence. The review concludes that effective object-detection systems must balance accuracy, speed, reliability, resource consumption, and responsible use according to the requirements of the intended application.