The convergence of large-scale visual-language models (LVLMs) has revolutionized object detection, enhancing adaptability, contextual inference, and generalization beyond traditional architectures. This in-depth review systematically explores the state-of-the-art in LVLMs through a three-stage research review process. First, we discuss the capabilities of VLMs for object detection and explain how they leverage natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. Next, we describe recent architectural innovations, training paradigms, and output flexibility in LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. This review thoroughly examines approaches used to integrate visual and textual information and demonstrates advances in object detection using VLMs, which facilitate more sophisticated object detection and localization strategies. This review provides comprehensive visualizations demonstrating the effectiveness of LVLMs in various scenarios, including localization and segmentation, and then compares their real-time performance, adaptability, and complexity against existing deep learning systems. Based on this review, we anticipate that LVLM will soon match or surpass the performance of existing methods in object detection. Furthermore, we identify several key limitations of current LVLM models, propose solutions to address these challenges, and present a clear roadmap for future advancements in this field. Based on this study, we conclude that recent advances in LVLM have had a revolutionary impact on object detection and robotics applications and will continue to do so.
