YOLO Object Detection on Android for Robotics
YOLO Object Detection on Android for Robotics Introduction Object detection is an important capability for autonomous robots. A robot can use detections to identify people, vehicles, tools, obstacles, and other objects in its environment. YOLO-family models are widely used for real-time object detection. In this tutorial, we will design an Android application that captures camera frames and…
This article outlines a tutorial for implementing YOLO object detection on Android for robotics applications. The system architecture consists of CameraX for capturing camera frames, preprocessing, running YOLO model inference, postprocessing, and a robot perception layer for interpreting the results. The Android project should be organized into separate layers to prevent tight coupling between camera handling, inference, and rendering.
A Kotlin data class is suggested for representing detected objects with properties like class ID, label, confidence, and bounding box. CameraX's ImageAnalysis is recommended for obtaining frames, with backpressure strategies needed for real-time robotics applications. Preprocessing steps like resizing, normalization, and tensor creation must match the model's requirements.
The detector abstraction allows running inference outside the main thread, and postprocessing steps include confidence filtering, class filtering, bounding-box conversion, and non-maximum suppression. Finally, the detection results can be drawn as bounding boxes over the live camera preview to aid in robotics applications.
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