Object Detection on Android for Autonomous Robots

A developer detailed a method for implementing object detection on Android devices to enable autonomous robots to recognize objects locally, reducing reliance on network connectivity. The approach uses CameraX for frame capture, a mobile inference runtime such as TensorFlow Lite or ONNX Runtime, and postprocessing techniques like non-maximum suppression. The system separates AI perception from robot control, allowing integration with navigation and safety systems.

Autonomous robots need to recognize objects in their environment. Object detection can identify people, vehicles, tools, signs, and obstacles from camera frames. Android can perform edge inference locally, reducing dependency on network connectivity. CameraX | Preprocessing | Object Detection Model | Postprocessing | Detection Results | Robot Perception Gateway Define a reusable result type: data class Detection val label: String, val confidence: Float, val left: Float, val top: Float, val right: Float, val bottom: Float This keeps the rest of the application independent from a particular model runtime. The camera analyzer should process frames asynchronously: php imageAnalysis.setAnalyzer executor { image - detector.detect image image.close } Use a latest-frame strategy when real-time responsiveness is more important than processing every frame. The detector can be implemented behind an interface: interface ObjectDetector { suspend fun detect frame: ImageFrame : List<Detection } Possible mobile inference approaches include TensorFlow Lite or ONNX Runtime, depending on the model and deployment requirements. Not every prediction should be passed to the navigation system. val valid = detections.filter { it.confidence = 0.6f } The threshold should be evaluated against the target environment rather than chosen arbitrarily. Detection models may produce overlapping predictions. Prediction A ─────── Prediction B ─────── ↓ NMS ↓ Single Detection Use the postprocessing method expected by your selected model. The Android device can send detections to the robot: { "label": "person", "confidence": 0.94, "bbox": 120, 80, 350, 500 } For autonomous navigation, the robot should combine this with physical measurements such as depth or LiDAR when distance matters. Instead of detecting every object from scratch at every stage, an additional tracking layer can maintain object identities between frames. Detection ↓ Tracking ↓ Object ID ↓ Navigation / Behavior Tracking can reduce redundant processing and provide temporal context. Important optimization techniques include: Keep AI perception separate from robot control: Camera ↓ Object Detection ↓ Perception State ↓ Navigation / Behavior ↓ Safety Controller ↓ Robot This separation makes the system easier to test and safer to operate. Evaluate the system using representative scenarios: Measure both detection accuracy and real-time performance. Object detection on Android can provide useful edge perception for autonomous robots. Kotlin, CameraX, and a mobile inference runtime create a flexible foundation that can later be connected to ROS 2, sensor fusion, navigation, and Physical AI agents. SDK Flutter: https://github.com/v-modal/vmodal sdk flutter https://github.com/v-modal/vmodal sdk flutter SDK Android: https://github.com/v-modal/vmodal sdk android https://github.com/v-modal/vmodal sdk android Discord: https://discord.gg/K72z28KUx https://discord.gg/K72z28KUx