📌 ONNX YOLOv8 Android Object Detection App

A real-time object detection Android application powered by YOLOv8 and ONNX Runtime.

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🌐 Project Overview

This Android application demonstrates object detection using a YOLOv8 model exported to ONNX format. It takes an image input, runs inference with ONNX Runtime, and displays results with bounding boxes, class labels, and confidence scores.

✅ Highlights

• Works offline – no network dependency.
• Lightweight and mobile-optimized.
• Extensible and cleanly structured codebase.

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✨ Key Features

🔍 YOLOv8 Inference (ONNX)

• Loads and executes a YOLOv8n model in ONNX format.
• Runs inference with efficient mobile performance via ONNX Runtime.

🖼️ Image Processing

• Preprocessing: Resize to 640×640, normalize to [0, 1] range.
• Postprocessing: Applies confidence filtering and Non-Maximum Suppression (NMS).

🧠 Detection Output

• Draws bounding boxes with class names and confidence values.
• Supports dynamic class names from labels.txt.

📱 Android Optimized

• Designed for Android 7.0+ (API 24+).
• Uses native bitmap processing for visualization.

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⚙️ Technologies Used

Technology	Description
Android SDK	Core Android app development
Java	Main development language
ONNX Runtime	Executes the YOLOv8 ONNX model
Bitmap/Canvas API	For drawing detections on images

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🚀 Setup & Installation

🔧 Prerequisites

• Android Studio (latest stable)
• Android device or emulator (API 24+)
• yolov8n.onnx and labels.txt (in res/raw/)

📥 Installation Steps

git clone https://github.com/your-repo/onnx-yolov8-android.git
cd onnx-yolov8-android

🎯 Conclusion

This Android app showcases the integration of YOLOv8 object detection via ONNX Runtime. It is ideal for mobile AI projects and can serve as a foundation for more advanced applications, including real-time video analysis, robotics, and industrial automation.

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