Two-Stage Versus One-Stage Architectures for Detecting Small, Distant Ground Vehicles on Edge Hardware: A Controlled Multi-Seed Evaluation of Accuracy, Speed, and Corruption Robustness

Detecting small, distant ground vehicles in real time on edge hardware requires models that jointly balance accuracy, computational efficiency, and robustness to degraded imagery; therefore, deployment suitability cannot be inferred from accuracy on general-purpose benchmarks alone. This study compares five detection architectures spanning two-stage and one-stage families under a standardized experimental protocol, using a purpose-built dataset of 5501 annotated frames covering three classes of ground vehicles. Each configuration was trained with three random seeds using a leakage-controlled data split, evaluated under a unified detection protocol, benchmarked directly on an NVIDIA Jetson Orin Nano Super, and tested using a reproducible, probability-based corruption protocol. To the best of our knowledge, no previous study has evaluated both detector families on a common ground-vehicle dataset dominated by small and distant instances, within a single framework combining multi-seed variance analysis, on-device performance measurement, controlled corruption testing, and training-protocol sensitivity analysis. Under each architecture’s canonical training regime, YOLO11n (mAP@0.5 = 0.811 ± 0.004) and YOLOv8n (0.807 ± 0.010) achieved comparable detection accuracy, outperforming the strongest two-stage configuration (0.775 ± 0.003) with substantially fewer parameters; disabling YOLO’s canonical online augmentation reversed this ranking. On the target edge platform, the one-stage models reached up to 61 FPS under TensorRT deployment, whereas Faster R-CNN configurations achieved, at most, 2.6 FPS. Under image corruption, Faster R-CNN retained 82.6% of its clean accuracy without corruption-specific training, exceeding YOLO’s 74.6–74.8% retention even after corruption-aware retraining from a 58.7–60.0% baseline. These results show that newer detector generations do not necessarily yield measurable gains in accuracy and that rankings can depend on training protocol choices; architecture selection should instead jointly weigh accuracy, latency, computational cost, and robustness. Under the evaluated conditions, compact one-stage detectors remain the most favorable deployment trade-off, while the standardized framework offers a reproducible basis for evaluating future architectures and edge platforms.