{"slug": "yolo-object-detection-on-android-for-robotics", "title": "YOLO Object Detection on Android for Robotics", "summary": "A developer detailed how to build an Android application that performs real-time YOLO object detection for robotics, using CameraX for frame capture and a mobile inference runtime for local processing. The tutorial emphasizes modular architecture, proper preprocessing, and postprocessing techniques like non-maximum suppression, while warning against treating 2D bounding boxes as physical measurements. It also highlights the importance of consistent frame rates and end-to-end latency for edge robotics applications.", "body_md": "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.\n\nYOLO-family models are widely used for real-time object detection. In this tutorial, we will design an Android application that captures camera frames and performs YOLO inference locally.\n\n```\nCameraX\n   |\nPreprocessing\n   |\nYOLO Model\n   |\nPostprocessing\n   |\nBounding Boxes\n   |\nRobot Perception Layer\n```\n\nThe exact model format depends on the runtime you choose. For Android edge deployment, an exported model may be converted to a mobile-compatible format and executed using an appropriate inference runtime.\n\nOrganize the project into separate layers:\n\n```\nvision/\n├── CameraManager.kt\n├── YoloDetector.kt\n├── Detection.kt\n└── DetectionOverlay.kt\n```\n\nThis prevents camera handling, inference, and rendering from becoming tightly coupled.\n\nCreate a Kotlin model:\n\n```\ndata class Detection(\n    val classId: Int,\n    val label: String,\n    val confidence: Float,\n    val boundingBox: RectF\n)\n```\n\nUse CameraX `ImageAnalysis`\n\nto obtain frames.\n\n``` php\nimageAnalysis.setAnalyzer(executor) { image ->\n    detector.process(image)\n    image.close()\n}\n```\n\nFor real-time robotics, use a backpressure strategy that drops stale frames rather than allowing an inference queue to grow indefinitely.\n\nMost object-detection models expect a fixed input size.\n\nThe preprocessing stage normally performs:\n\nFor example:\n\n```\nCamera Frame\n   ↓\nResize\n   ↓\nNormalize\n   ↓\nTensor\n   ↓\nYOLO\n```\n\nThe preprocessing code must match the model's training/export requirements.\n\nCreate a detector abstraction:\n\n```\nclass YoloDetector {\n    suspend fun detect(frame: ImageFrame): List<Detection> {\n        // preprocess\n        // inference\n        // postprocess\n        return emptyList()\n    }\n}\n```\n\nRun inference outside the Android main thread.\n\nObject detectors can return multiple candidate boxes. Postprocessing commonly includes:\n\nFor example:\n\n```\nRaw Predictions\n      ↓\nConfidence Filter\n      ↓\nNMS\n      ↓\nFinal Detections\n```\n\nThe Android UI can display detection results over the live camera preview.\n\n```\n+--------------------------+\n|                          |\n|     +------------+       |\n|     |   bottle   |       |\n|     |    92%     |       |\n|     +------------+       |\n|                          |\n+--------------------------+\n```\n\nRemember to map coordinates correctly when the preview and model input have different aspect ratios.\n\nDetection results can be passed to a robot control layer:\n\n```\n{\n  \"object\": \"person\",\n  \"confidence\": 0.92,\n  \"bbox\": [120, 80, 350, 500]\n}\n```\n\nThe robotics layer can combine this information with depth, odometry, LiDAR, or other sensors.\n\nDo not treat a 2D bounding box as a physical distance measurement unless the system has additional calibration or depth information.\n\nFor edge robotics:\n\nA smaller model running consistently can be more useful for robotics than a larger model with unstable frame rates.\n\nTest the detector with:\n\nFor robotics, also measure end-to-end latency:\n\n```\nCapture → Inference → Decision → Robot Command\n```\n\nYOLO-style object detection can turn an Android device into a useful edge-vision component for robotics. Kotlin, CameraX, and a mobile inference runtime provide the foundation for building perception prototypes that can later integrate with ROS 2 and autonomous navigation.\n\nSDK Flutter: [https://github.com/v-modal/vmodal_sdk_flutter](https://github.com/v-modal/vmodal_sdk_flutter)\n\nSDK Android: [https://github.com/v-modal/vmodal_sdk_android](https://github.com/v-modal/vmodal_sdk_android)\n\nDiscord: [https://discord.gg/K72z28KUx](https://discord.gg/K72z28KUx)", "url": "https://wpnews.pro/news/yolo-object-detection-on-android-for-robotics", "canonical_source": "https://dev.to/vmodal_ai/yolo-object-detection-on-android-for-robotics-2bde", "published_at": "2026-08-17 20:36:18+00:00", "updated_at": "2026-08-17 21:14:02.316371+00:00", "lang": "en", "topics": ["computer-vision", "artificial-intelligence", "machine-learning", "robotics", "developer-tools"], "entities": ["YOLO", "CameraX", "Kotlin", "ROS 2", "v-modal"], "alternates": {"html": "https://wpnews.pro/news/yolo-object-detection-on-android-for-robotics", "markdown": "https://wpnews.pro/news/yolo-object-detection-on-android-for-robotics.md", "text": "https://wpnews.pro/news/yolo-object-detection-on-android-for-robotics.txt", "jsonld": "https://wpnews.pro/news/yolo-object-detection-on-android-for-robotics.jsonld"}}