Comprehensive review of recent developments in visual object detection based on deep learning

This comprehensive review looks into the recent developments in visual object detection, focusing on the transformative effect of deep learning (DL) technologies. In object detection, computer vision is a basic issue. This involves object detection and location in the video and image frames, which has notable advantages in robotics, autonomous driving, medical imaging, and surveillance. This review, therefore, presents a thorough integration analysis in visual object detection of the latest developments, providing both the historical context and state-of-the-art analysis. This review categorizes current methods into one-stage and two-stage frameworks, studying their architectural innovations, detection accuracy, computational speed, and deployment readiness. This review further scrutinizes the performance measures, emphasizes the inevitability of large-scale annotated datasets, and provides a curated overview of the widely used datasets in the field. Notable features include a discussion of practical applications and current research trends, and a comprehensive comparative analysis that compares models based on accuracy, speed, and trade-offs. A unique addition of this work is a thorough comparative analysis table that benchmarks traditional and modern models in terms of mean Average Precision (mAP), frames per second (FPS), advantages, limitations, and the coverage of transformer-based models and real-time deployments. The review’s holistic approach provides significant insights for researchers and practitioners seeking to understand, benchmark, develop, or benchmark object detection systems.