Object Detection in Computer Vision: A Comprehensive Review of Algorithms, Datasets, and Evaluation Techniques
Year: 2026
Type: review
Abstract: Object detection has evolved dramatically over three decades, transitioning from handcrafted features to deep learning, transformers, and now large language models. This paper provides a comprehensive systematic review of this evolution, covering traditional methods (SIFT, HOG, Viola-Jones), CNN-based single- and two-stage detectors (R-CNN family, YOLO v1-v12, SSD), transformer-based architectures (DETR, ViT, Swin), and emerging LLM-driven approaches (GPT-4V, Grounding DINO, YOLO-World). We benchmark and compare these methods using key metrics-mAP, IoU, FPS-and analyze over 50 datasets across generic, autonomous driving, medical, remote sensing, and agricultural domains. Our critical synthesis identifies persistent research gaps: lightweight edge deployment, sensor fusion for real-time systems, bias and fairness in AI, and multimodal vision-language integration. By consolidating three decades of progress and outlining future challenges, this survey serves as a valuable resource for researchers and practitioners seeking to develop accurate, efficient, and reliable object detection systems.
Source: Sensors
DOI: 10.3390/s26206663
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7204770977