When Does Supervised Training Pay Off? The Hidden Economics of Object Detection in the Era of Vision-Language Models

Object detection constitutes a foundational computer vision capability enabling diverse applications from autonomous vehicles to retail analytics, with modern deep learning approaches achieving remarkable technical performance exceeding 90% mean Average Precision on standardized benchmarks [1, 2]. However, technical accuracy represents only one dimension of deployment viability, as real-world system selection requires evaluating cost-effectiveness--the relationship between detection performance and total economic investment required to achieve that performance [3, 4]. Traditional supervised detectors, exemplified by the YOLO architecture family [2, 5], rely fundamentally on manually annotated training data, with industry reports estimating annotation costs between $0.10 and $0.50 per bounding box [6, 7], translating to $9,000-$45,000 for establishing 100-category detection systems with sufficient training data. Vision-Language Models represent an alternative paradigm achieving object detection through zero-shot inference without task-specific supervision [8-10]. Pre-trained on billions of image-text pairs, VLMs accept natural language object descriptions and generate bounding box predictions through learned visual-linguistic alignment, fundamentally eliminating annotation requirements.