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

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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.