Mobile Object Detection: YOLO, TFLite, Tracking on iOS & Android

We implement object detection in mobile apps—not just finding objects, but tracking between frames, projecting bounding boxes onto the preview layer, handling overlaps, and maintaining 30 FPS. Real-time on a mobile device is a tough trade-off: the model must output detection in 20–30 ms to avoid dropping frames, yet not overheat the CPU or drain the battery. We account for OS version, available delegates (GPU, NNAPI, Core ML), and confidence thresholds. We solve this from architecture selection (YOLO, SSD, NanoDet) to final integration with tracking, projection, and store publication. Below are specific techniques we apply in projects.

How to Choose a Model for Mobile Detection?

Model choice depends on target FPS, supported hardware, and accuracy requirements. Let's examine three popular architectures: MobileNet SSD, YOLOv8n, and NanoDet. MobileNet SSD balances speed and accuracy, well-optimized for TFLite, supports int8 quantization. YOLOv8n gives better quality (mAP 37+) with a deeper architecture but requires a GPU delegate for comfortable use. NanoDet is lightweight for weak devices without GPU, but accuracy is limited.

The COCO dataset shows YOLOv8n provides 30% higher mAP than MobileNet SSD at comparable latency. YOLO remains the real-time standard. NanoDet, conversely, loses accuracy but is 2x faster on weak devices.

Why Is Tracking Critical for Object Detection?

Detecting every frame is expensive. The right approach: detect once every N frames (usually every 5–10), in between use tracking via SORT or ByteTrack, or built-in VNDetectRectanglesRequest with ObjectTrackerObservation on iOS. ML Kit Object Detection & Tracking supports tracking out of the box with .enableMultipleObjects() and .enableClassification(). Each tracked object gets a stable trackingID—this allows displaying object info without flickering on loss/reappearance.

NMS (Non-Maximum Suppression) is a key parameter. Default iouThreshold = 0.5. If objects overlap (e.g., packed items on a conveyor), the threshold should be lowered to 0.3–0.35. Otherwise the detector merges neighboring objects into one. Recommended thresholds:

How to Set Up Bounding Box Projection for iOS and Android?

The most common visual error is bounding box misaligned with the object on preview. Reason: the model receives a resized image (e.g., 320×320), while the camera preview is 1920×1080 with AspectFill or AspectFit. Coordinates must be recalculated with scale and offsets.

On iOS with AVCaptureVideoPreviewLayer:

let converted = previewLayer.layerRectConverted(fromMetadataOutputRect: normalizedRect)

VNDetectedObjectObservation returns boundingBox in normalized coordinates (0..1, y from bottom). Before projecting to UIKit coordinates, invert the Y-axis: CGRect(x: box.minX, y: 1 - box.maxY, width: box.width, height: box.height). Apple's AVCaptureVideoPreviewLayer documentation explains this.

On Android with CameraX + ImageAnalysis: detection results are in input image coordinates, preview is in PreviewView coordinates. Use ML Kit's MappingUtils or compute transformation manually via matrix.

How to Achieve 30 FPS?

Beyond model choice, key factors are quantization (int8 vs float), delegate selection (GPU, NNAPI, Core ML), and detection frequency. On iOS with Core ML, use .computUnit = .gpuAndNeuralEngine. On Android, GPUDelegate with PrecisionLossAllowed. For weak devices, use NNAPI. We test on real devices, measure FPS and CPU temperature. We guarantee stable 30 FPS on flagships and 15–20 FPS on mid-range devices.

Our Experience and Guarantees

With 5+ years of work, we have implemented object detection in 50+ projects—from retail (shelf item counting) to security (people and vehicle detection). Our engineers are familiar with App Store Review Guidelines (Section 4.2, 5.1) and Google Play requirements for camera apps, simplifying publication. We also guarantee post-integration support: answer questions, fix bugs, help with model retraining.

What's Included in the Work

• Model selection and adaptation for your device (iOS/Android)
• Camera integration (CameraX, AVCaptureSession)
• Correct bounding box projection on preview
• Tracking and NMS configuration
• Testing on real devices and optimization up to 30 FPS
• Documentation and store publication recommendations

Work Process

1. Analysis: Review your use cases and target devices.
2. Design: Choose model architecture, delegate, and post-processing parameters.
3. Implementation: Integrate model with camera, set up projection and tracking.
4. Testing: Measure FPS, accuracy, power consumption on 5+ devices.
5. Deployment: Prepare build, documentation, and assist with App Store / Google Play submission.

Timelines and How to Start

Integration of a ready model with projection and tuning takes 1–2 weeks. Retraining on custom classes adds 1–2 weeks. We'll assess your project for free—contact us. Get a consultation on object detection in your app. Reach out to discuss your scenario.

Machine Learning in Mobile Apps: CoreML, TFLite, and On-Device Models

We distinguish two fundamentally different approaches: an app with on-device AI and an app that simply calls a cloud API. The former works without internet, does not send user data to third-party servers, and responds within 50 milliseconds. The latter depends on network latency and pricing plans. Choosing the architecture is a key step that directly affects cost, privacy, and user experience in machine learning in mobile apps. Our experience shows that in 70% of projects, on-device inference is cheaper in the long run due to eliminating server costs.

How to Choose Between CoreML and TFLite for On-Device Inference?

CoreML — Apple's native framework for running ML models on device. Supports Neural Engine (starting with A11 Bionic), GPU, and CPU as fallback. Models are converted to .mlmodel format via coremltools from PyTorch, ONNX, or TensorFlow. Conversion is not always trivial: custom layers require implementing MLCustomLayer, and INT8 quantization can sometimes noticeably reduce accuracy on specific data. We ensure the final model passes validation on real data before and after conversion.

TensorFlow Lite — cross-platform alternative for Android and Flutter. On Android it uses NNAPI (Neural Networks API) for hardware acceleration — since Android 10 NNAPI is more stable; before that it's better to explicitly use GPU delegate via GpuDelegate. A typical mistake: the model is trained on normalized data in range [0,1], but the app feeds [0,255] — inference runs but produces meaningless results without any error. We include an automatic input data validation module in the SDK.

For image classification, object detection, and segmentation tasks, ready-to-use optimized models are available. YOLOv8 in CoreML format runs detection on a 640×640 frame in 15–20 ms on iPhone 14 Neural Engine. MobileNetV3 on TFLite with GPU delegate runs around 8 ms on Pixel 7 for classification.

What If You Need Text Generation On-Device?

Running small language models on device has become a reality in the last few years. Apple Intelligence uses its own models via Private Cloud Compute, but for third-party developers other paths are available.

llama.cpp with Metal backend on iOS is a working approach for phi-3-mini (3.8B parameters, 4-bit quantization, ~2.3 GB). Inference: 15–25 tokens/second on iPhone 15 Pro. For integration in Swift, use the Swift Package llama.swift or a wrapper via C interface llama.h. The binary is not bundled with the app — the model is downloaded on first launch and stored in Application Support. Our certified developers configure incremental download to avoid blocking the first launch.

On Android, the analog is Google AI Edge (formerly MediaPipe LLM Inference API) supporting Gemma-2B. It works via GPU delegate, on Tensor G3 chip Pixel 8 Pro — about 20 tokens/second.

Limitations are real: models larger than 4B parameters are still slow on mobile devices. For complex reasoning tasks, on-device LLM falls behind GPT-4o in quality. A hybrid approach — on-device for short tasks and private data, cloud for complex queries — is often optimal. We will evaluate your case and propose a balance of performance and privacy — contact us.

How Does On-Device Inference Compare to Cloud in Terms of Cost and Performance?

On-device inference is typically 10x cheaper per request than cloud APIs for image recognition tasks, while also eliminating latency variability and privacy risks. The table below summarizes the trade-offs.

For an app with 100k MAU running 10 image recognitions per user per month, on-device inference can save up to $5,000 monthly compared to cloud API. Get a free consultation on your ML architecture today.

Integrating OpenAI API and Other Cloud Models

For scenarios where cloud inference is acceptable, integrating OpenAI, Anthropic, or Google Gemini is an HTTP client + streaming SSE. In Swift, AsyncThrowingStream is convenient for streaming responses. In Kotlin, use Flow.

Critically: API keys must never be stored in the app bundle. Even an obfuscated key can be extracted from the IPA in 10 minutes using strings or frida. Correct architecture: mobile app → your own backend → OpenAI API. The backend controls rate limiting, logs requests, and protects the key.

What Is Included in the Work (Deliverables)

• Trained and quantized model for the target device (documentation with metrics)
• SDK for integration (Swift/Kotlin/Flutter) with call examples
• Performance tests on 3–5 real devices
• Instructions for OTA model updates
• Support during App Store / Google Play moderation (compliance with Guidelines 4.2, 5.1)
• 2 weeks of technical support after release

Typical Project Pipeline

1. Task analysis — measure latency, privacy, size, supported devices.
2. Model prototyping — in Python, evaluate accuracy on target data.
3. Conversion and quantization — for CoreML/TFLite with validation.
4. Integration into the app — model wrapped in a service layer (easy to swap CoreML ↔ TFLite ↔ cloud).
5. Testing — on real devices, measure FPS, RAM, battery.
6. Deployment — via TestFlight / Firebase App Distribution, monitor metrics.

Timelines: integration of a ready CoreML/TFLite model — 1–2 weeks, development of a custom model with mobile optimization — from 6 weeks, on-device LLM chat with personalization — 4–8 weeks.

Why We Take on Complex Cases?

10+ years of experience in mobile development, 50+ implemented AI/ML solutions, guarantee of compatibility with current iOS and Android versions. All projects undergo code review and load testing. The cost includes preparation of moderation documentation and training of your team.

Contact us — we will help you choose the architecture and implement ML in your app turnkey. Order an audit of your existing solution — we will assess the potential for server cost savings free of charge. In some projects, savings can reach significant amounts per month.