On-device AI has fundamentally transformed how we approach real-time object detection, shifting processing from cloud servers to the hardware in our pockets and edge devices. This transformation enables applications ranging from autonomous vehicles to mobile surveillance systems while maintaining low latency and preserving user privacy. Understanding how on-device object detection works and comparing different approaches helps developers and organizations choose the right solution for their specific needs.

Why On-Device Object Detection Matters #

Real-time object detection has traditionally relied on cloud computing, which introduces latency, bandwidth costs, and privacy concerns. On-device inference processes data locally without sending sensitive information to external servers, reducing response times and protecting user privacy.[6] For applications requiring immediate decisions—such as autonomous driving or live video analysis—cloud-based detection is impractical. Modern edge devices like iPhones, Raspberry Pi units, and NVIDIA Jetson boards now possess sufficient computational power to run sophisticated detection models efficiently.

The choice of deployment location and model architecture directly impacts system performance. A 10 FPS detection with accurate custom training often outperforms 60 FPS generic detection for specific use cases, demonstrating that speed alone doesn’t guarantee better results.[1] This principle guides the selection of appropriate models and deployment strategies.

Major Object Detection Models for On-Device Deployment #

YOLO Family: The Real-Time Standard #

The YOLO (You Only Look Once) series represents the most widely adopted approach for real-time object detection. YOLO’s single-shot detection method processes entire images in one forward pass, making it fundamentally efficient compared to multi-stage approaches.[2] The latest iteration, YOLOv12, introduced attention-based mechanisms including Area Attention and FlashAttention, achieving higher mean Average Precision (mAP) across all scales while maintaining or improving inference latency.[5]

Strengths of YOLO models:

• Proven real-time performance with YOLO11 achieving 53.4% mAP on COCO while maintaining 200+ FPS on GPUs[1]
• Extensive model variants (Nano, Small, Medium, Large, XLarge) allowing flexible speed-accuracy tradeoffs[1]
• When quantized for iOS Neural Engine, YOLO models easily achieve 60+ FPS for live video[1]
• Effective small object detection through grid-based architecture[2]
• Superior accuracy in specialized domains like traffic management (76.5% top-1 accuracy for vehicle detection)[4]

Limitations:

• Can struggle with small objects or closely-spaced objects that occupy the same grid cell[4]
• Accuracy degradation occurs on edge devices with aggressive compression, particularly on Raspberry Pi platforms with TPUs[3]

RF-DETR: The iOS-Optimized Choice #

RF-DETR, released in March 2025, represents Roboflow’s state-of-the-art real-time detection model specifically designed for on-device deployment.[1] Despite using transformer architecture—traditionally computationally expensive—RF-DETR achieves 54.7% mAP at just 4.52ms latency on T4 GPUs while maintaining strong accuracy when quantized to INT8 format for iOS.[1]

Strengths:

• First real-time transformer model to exceed 60 mAP on domain adaptation benchmarks[1]
• Quantization-friendly design maintaining strong accuracy despite 75% model size reduction through INT8 quantization[1]
• Native Swift SDK support for seamless iOS integration[1]
• Production-ready speeds on edge devices

Considerations:

• Relatively recent release means less community resources and real-world deployment data compared to YOLO
• Transformer-based architecture may require more specialized knowledge for fine-tuning

Efficient Alternatives: MobileNet and EfficientDet #

For severely resource-constrained environments, EfficientDet Lite and SSD MobileNet V1 offer extreme efficiency gains. These models consume significantly less energy and achieve faster inference times than higher-accuracy alternatives.[3] SSD MobileNet V1, despite achieving lower mAP scores, excels in energy efficiency and inference speed on platforms like Raspberry Pi 3.[3]

Strengths:

• Minimal power consumption enabling extended battery life on mobile devices
• Fast inference times even on older hardware
• Smaller model footprints suitable for app distribution

Trade-offs:

• Significantly lower accuracy compared to YOLO or RF-DETR variants
• Better suited for non-critical applications or scenarios where speed outweighs accuracy requirements

Key Performance Considerations for On-Device Deployment #

Device-Specific Factors #

Different hardware platforms dramatically affect model performance. Raspberry Pi devices show notably poorer results compared to more powerful platforms like Jetson Orin Nano, which achieves the best inference times and accuracy for YOLOv8 models.[3] The Neural Engine on iPhones provides efficient inference at high frame rates when models are properly quantized, while thermal management becomes critical during extended use—smaller models run cooler, maintaining consistent performance as devices throttle performance when overheating.[1]

Power and Thermal Efficiency #

Battery life remains a primary concern for mobile applications. A model running at 30 FPS consumes half the power of a model running at 15 FPS due to the Neural Engine’s efficiency characteristics.[1] This counterintuitive relationship means that faster inference often translates to better overall power efficiency despite higher frame rates. Thermal management compounds this benefit, as smaller models generate less heat, preventing performance throttling on extended use.

Model Size and Storage #

Application bundle size impacts user experience and adoption. Deploying multiple large models or requiring on-device model downloads strains user storage, creating friction in the installation and update process. Smaller, quantized models address these constraints while maintaining reasonable accuracy for most applications.[1]

Comparison Framework #

Deployment Strategy Recommendations #

For maximum accuracy with real-time performance: YOLO11 or RF-DETR offer the best balance, with RF-DETR providing native iOS advantages. These models suit applications like autonomous vehicles, advanced surveillance, or medical imaging where accuracy drives business value.

For battery-critical mobile applications: Smaller YOLO variants (Nano, Small) or EfficientDet Lite models provide reasonable accuracy with minimal power consumption, ideal for fitness tracking, wildlife monitoring, or energy-conscious consumer applications.

For extreme resource constraints: SSD MobileNet V1 delivers the fastest inference with minimal power consumption, though accuracy compromises make it suitable only for basic detection tasks or scenarios with high redundancy tolerance.

For iOS-native development: RF-DETR’s Swift SDK integration reduces development complexity and maximizes platform utilization, making it an excellent choice for iOS-first products despite being newer to market.

Practical Latency Considerations #

On-device inference proves most advantageous under strict latency constraints below 0.3 seconds, matching or exceeding cloud-based and cooperative inference approaches.[6] As acceptable latency increases to 1.0 second, cloud-based solutions become more power-efficient due to batch processing advantages. This creates a clear decision boundary: applications requiring rapid response times should prioritize on-device deployment, while applications tolerating slight delays might benefit from hybrid approaches.

Conclusion #

On-device real-time object detection has matured from theoretical possibility to practical necessity in mobile and edge computing. The choice between YOLO variants, RF-DETR, and efficient alternatives depends on specific constraints: accuracy requirements, available hardware, power budgets, and development platform preferences. YOLO11’s proven performance and extensive ecosystem support make it the default choice for most applications, while RF-DETR represents the cutting edge for iOS development. Smaller models provide viable alternatives when power and size constraints dominate accuracy requirements. Modern developers should evaluate their specific constraints and test multiple candidates rather than assuming fastest or most accurate equals best, as context fundamentally determines the optimal solution.