The YOLOv series represents state-of-the-art technology for single-stage object detection, excelling in speed and accuracy. In many scenarios, it outperforms traditional two-stage detection frameworks, making it ideal for real-time applications. This study evaluates YOLOv11 model variants (n, s, m, i, x) on a custom dataset of 2,285 labelled images representing four delivery vehicle classes: FedEx, Other-Vehicles, UPS, and USPS-Truck. The dataset is meticulously curated to capture diverse delivery vehicle scenarios and split into training, validation, and test sets. Each variant was fine-tuned using uniform settings: 20 epochs, an input resolution of 640×640 pixels, and a batch size of 16.