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iOS DevelopmentCore MLComputer Vision
Object Detection
iOS Application.
A high-performance mobile application leveraging Apple's Vision framework and Core ML to perform real-time spatial recognition and edge-based neural processing.
Project Overview
The objective was to create a seamless, low-latency mobile experience that could identify and track objects in real-time without relying on cloud-based processing. By utilizing on-device machine learning, the app ensures user privacy while delivering instantaneous results.
The implementation involved optimizing YOLO-based models for mobile deployment and integrating them with the camera's high-frequency output stream.
Core Challenges
- 01. Thermal Management during sustained ML workloads.
- 02. Dynamic UI rendering for multiple simultaneous detections.
- 03. Maintaining 60FPS camera passthrough with 30ms inference times.
The Solution
- 01. Quantized Core ML models to FP16 to reduce memory footprint.
- 02. Metal-accelerated drawing for bounding boxes and labels.
- 03. Async inference pipeline to decouple ML from UI rendering.
Stack Details
developer_mode
Swift / SwiftUI
Primary development language and UI framework.
psychology
Core ML
Integration of neural networks for on-device inference.
visibility
Vision Framework
High-level computer vision tasks and image analysis.