Description :

*Computer Vision and YOLO: Concepts, History, Architecture, Implementation, and Evaluation* examines the development of computational visual perception, from image processing and feature engineering to deep learning, transformers, and multimodal visual models. This book positions object detection as a bridge between theory and application, as it incorporates classification, localization, performance measurement, and the need for real-time inference.

The main discussion traces YOLOv1 through YOLO26, emphasizing changes in the backbone, neck, head, anchor, target assignment, loss, NMS, and NMS-free design. The book critically compares versions and emphasizes that YOLO's numbering stems from multiple lines of development. Therefore, readers learn to assess papers, documentation, benchmarks, licensing, and ecosystem maturity separately.

The implementation section presents Python examples for inference, custom training datasets, video tracking, ONNX export, and FastAPI integration. Case studies cover occupational safety, manufacturing inspection, product counting, agriculture, transportation, and healthcare. The final chapter discusses deployment, optimization, MLOps, privacy, robustness, governance, the final project, and the research agenda beyond 2026.