How to Integrate YOLOv11 in Android | Real-Time Object Detection App Tutorial
🚀 *YOLOv11 Android Demo — Real-Time Object Detection with INT8 TFLite Models*
In this video, I showcase the performance of *three YOLOv11 models* running directly on an *Android device**, optimized for **real-time detection* using *INT8 quantized TensorFlow Lite* models.
You’ll see:
1️⃣ *Helmet Detection Model* – Detects whether riders are wearing helmets or not.
2️⃣ *License Plate Detection + OCR* – Detects vehicle license plates and recognizes the text.
3️⃣ *Pre-trained YOLOv11 (COCO Dataset)* – Performs general object detection on everyday objects.
All models have been *quantized to INT8 precision* and converted to *TFLite* to achieve smoother inference on CPU-only devices — delivering faster performance while maintaining strong detection accuracy.
🎯 *Key Highlights:*
Real-time detection demo on Android
Multi-model showcase (helmet, license plate + OCR, COCO)
Optimized inference on mobile CPU
💡 This video focuses purely on **model performance and results**, without going into the conversion or setup process.
👇 *Chapters:*
00:00 Introduction
00:50 Assets package
02:05 Code walk through
07:20 NMS Code ( Non Max Suppression )
07:40 IOU Code ( Intersection of Union )
08:05 OCR Code ( Optical Character Recognition )
09:00 Pass Your Custom Model and Class
09:15 Demo
🔗 *Resources & Demo Code:*
https://github.com/DaaniDev/Yolov11App
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#YOLOv11 #AndroidAI #ObjectDetection #TFLite #INT8 #MachineLearning #ComputerVision