Case Study: Computer Vision

Intelligent Object
Detection iOS

A performance-driven mobile application developed by GOLDEN_KALALA, focused on real-time spatial recognition and edge-based neural processing for seamless user experiences.

SwiftUI AVFoundation Core ML Neural Engine
iOS Interface Mockup
warning

The Mobile AI Challenge

Mobile computer vision often suffers from high battery drain and significant lag when processing high-resolution video feeds. The primary hurdle was balancing detection accuracy with the thermal and power constraints of a handheld device, ensuring a "live" feel without frame drops.

architecture

Edge-Optimized Pipeline

By integrating SwiftUI for a responsive UI and AVFoundation for granular camera control, the solution bypasses standard overheads. The core of the application leverages Core ML to run inference locally, keeping data private and response times sub-millisecond.

  • bolt Direct camera buffer to Neural Engine pipeline
  • shield Local-only processing for maximum data privacy

Technical Architecture

Deterministic data flow from pixel to prediction.

videocam
AVFoundation
Raw Buffer Stream
ANE Execution
memory
Core ML / Vision
Quantized ResNet50
view_quilt
SwiftUI Layer
Overlay Rendering

Challenges & Engineering

Latency vs. Resolution

Processing 4K frames in real-time is computationally impossible for continuous inference. I implemented a down-sampling pre-processor that crops and resizes frames to the model's native input size (224x224) using Metal-backed acceleration to keep the UI at 60FPS.

Thermal Throttling

Continuous ANE usage generates heat. I introduced a dynamic inference frequency controller that scales prediction rates based on device temperature and remaining battery percentage.

Detector_Logic.swift

func captureOutput(_ output: AVCaptureOutput, didOutput ...)

guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else { return }


/ Run inference on background queue

let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer)

try? handler.perform([detectionRequest])


private func handleDetections(request: VNRequest) {

DispatchQueue.main.async {

self.updateUI(with: results)

}

}

Stack Details
SWIFTUI CORE ML 3 AVFOUNDATION METAL
15ms
Mean Inference Speed
97.4%
Precision Score
60FPS
UI Rendering Rate

Future Improvements

  • YOLOv8 Implementation: Migrating to YOLO for multi-object tracking and improved spatial bounding boxes.

  • MobileNet Backbones: Exploring lighter architectures to support older iOS hardware without performance degradation.

  • Spatial Audio Integration: Using ARKit for auditory object spatialization to assist visually impaired users.

Project Availability

This project is available for technical review on GitHub or via a custom TestFlight build for potential collaborators.

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