Find My Ball: Real-Time AI Golf Ball Detection on Mobile Devices
FMB wanted to build a mobile app that could automatically detect lost golf balls using the phone camera to help golfers quickly locate balls in challenging terrain.
The Challenge
Detecting golf balls in natural environments like tall grass or leaf piles is difficult due to the small object size and cluttered backgrounds. Additionally, the system needed to perform real-time detection entirely on-device without cloud dependency to ensure low latency and continuous availability during gameplay on various course terrains.
Our Solution
Architecture Overview: Mobile Edge Intelligence
Find My Ball represents a breakthrough in mobile computer vision, bringing production-grade object detection to the palm of a golfer's hand. By combining custom-trained vision models with deep hardware optimization, we achieved real-time detection that functions reliably in the most challenging environmental conditions without needing an internet connection.
Project Showcase
The end product is a sleek, intuitive iOS app available on the App Store. The entire complex object detection pipeline is seamlessly abstracted away from the user behind a clean interface.
Small Object Detection Optimization
Golf balls subtend a very small area on a standard mobile sensor (often less than 10x10 pixels at range). We implemented a specialized feature pyramid network (FPN) and tuned the anchor box scales to specifically target these micro-sized objects amidst complex textures like grass and sand.
- Custom Anchor Box Scaling
- Feature Pyramid Network (FPN)
- Texture-aware training data augmentation
Mobile Edge Inference (CoreML)
To ensure usability on-course without 5G/LTE reliance, the entire inference engine runs locally. We optimized a YOLO-based backbone using 8-bit quantization and converted it to CoreML, fully utilizing the iPhone's Neural Engine for sub-30ms inference.
- 8-bit INT8 Quantization
- Neural Engine Acceleration
- Zero cloud-dependency architecture
Temporal Smoothing & Tracking
Raw detections in outdoor lighting can be jittery. We integrated a temporal smoothing layer that cross-references detections across multiple frames, filtered through a Kalman filter to predict ball position during rapid camera movement or partial occlusion.
- Kalman Filter Integration
- Confidence-weighted frame averaging
- Handles partial occlusion in tall grass
Dynamic Exposure Management
Outdoor sports photography faces extreme dynamic range challenges. The app dynamically adjusts camera ISP settings based on the AI's zone of interest to maintain contrast on the white golf ball against bright sand or deep green grass shadows.
- Real-time ISP feedback loop
- Contrast-adaptive preprocessing
- Auto-exposure locking on AOI
In-Field Reliability
The system has been tested across hundreds of golf courses worldwide, maintaining high detection accuracy regardless of the grass type, lighting conditions, or smartphone model used.
The Impact
The solution enables golfers to locate lost balls instantly, improving pace of play and enhancing the overall golfing experience. It serves as a prime example of how edge AI can power intelligent, real-time mobile applications in challenging physical environments.
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