Using Object Detection in Augmented Reality for Game Development

Discover how object detection revolutionizes augmented reality in game development! This blog delves into leveraging advanced AI techniques to create immersive gameplay experiences. Explore practical examples and learn how AR and object detection can redefine interactive entertainment.

Table of Contents

Time-to-market is often framed as a delivery issue. For leadership, it is more so an architecture decision.

Organizations slow down when platforms are hard to change, releases require leadership sign-off at every step, or early design choices limit later decisions. In these situations, speed is limited not by execution effort, but by the cost of change. Cloud architecture affects time-to-market by lowering that cost and allowing business priorities to be acted on without structural delays.

Augmented Reality and Game Development with Google ML Kit

Reducing the Cost of Change Through Cloud Foundations

When cloud foundations are designed with intent, releases shift from infrequent, high-risk events to smaller, predictable updates. Changes can be introduced without reworking core systems, which gives leadership clearer timelines and the ability to respond to market or customer signals without disrupting ongoing operations.

Architecture-Driven Risk Management

Cloud architecture also reshapes how risk is managed. Performance, scalability, and reliability issues are identified earlier in the lifecycle, when they can be resolved without last-minute trade-offs. This reduces late-stage surprises and makes launches more controlled, rather than compressed under pressure.

Consistency at Scale as a Leadership Requirement

As organizations scale, speed alone is insufficient. Consistency becomes a leadership requirement. Cloud-based platforms enable common delivery patterns across teams and regions, reducing dependency on individual execution styles. For CXOs, this translates into greater predictability across initiatives, better portfolio-level planning, and fewer delivery escalations.

Observations from Yugensys in Practice

In practice, Yugensys has seen time-to-market improve when architectural choices are made with business outcomes in mind, not treated as mere implementation. Across product launches and modernization programs, this has typically resulted in:

1) Platforms structured to validate direction early, allowing leadership teams to confirm priorities before committing significant time or capital
2) Existing systems updated in specific high-impact areas, so releases become faster and more predictable without disrupting stable operations
3) Cloud foundations built to support growth when it occurs, rather than forcing premature investment

Alignment Over Urgency

Cloud architecture does not guarantee speed. But when aligned with business priorities, it removes many of the reasons products fail to reach the market on time.

At Yugensys, this alignment is treated as a discipline – one that helps leadership teams move with confidence, not urgency.

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Conclusion: Architecture as a Strategic Enabler

Ultimately, time-to-market improves when architecture, delivery, and leadership intent move in the same direction. Cloud decisions made in isolation may modernize systems, but decisions made in alignment with business priorities create momentum that sustains growth.

This is where architecture stops being a technical concern and becomes a strategic lever—enabling organizations to act decisively, adapt continuously, and bring products to market with clarity and control.

Picture of Vaishakhi Panchmatia

Vaishakhi Panchmatia

As the Tech Co-Founder at Yugensys, I’m driven by a deep belief that technology is most powerful when it creates real, measurable impact. At Yugensys, I lead our efforts in engineering intelligence into every layer of software development — from concept to code, and from data to decision. With a focus on AI-driven innovation, product engineering, and digital transformation, my work revolves around helping global enterprises and startups accelerate growth through technology that truly performs. Over the years, I’ve had the privilege of building and scaling teams that don’t just develop products — they craft solutions with purpose, precision, and performance.Our mission is simple yet bold: to turn ideas into intelligent systems that shape the future. If you’re looking to extend your engineering capabilities or explore how AI and modern software architecture can amplify your business outcomes, let’s connect.At Yugensys, we build technology that doesn’t just adapt to change — it drives it.

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Using Object Detection in Augmented Reality for Game Development

Discover how object detection revolutionizes augmented reality in game development! This blog delves into leveraging advanced AI techniques to create immersive gameplay experiences. Explore practical examples and learn how AR and object detection can redefine interactive entertainment.

Table of Contents

Introduction

Augmented reality (AR) has transformed the gaming industry, offering players immersive experiences that blend the virtual and real worlds seamlessly. One of the key technologies driving AR gaming is object detection, which allows games to recognize and interact with real-world objects captured by a device’s camera. In this blog post, we’ll explore how object detection is used in game development, diving into a codebase that demonstrates its implementation.
Augmented Reality and Game Development with Google ML Kit

Object Detection in Gaming

Object detection involves identifying and locating specific objects within an image or video frame. In the context of gaming, object detection enables developers to create experiences where virtual objects are overlaid onto the real world, enhancing player interaction and immersion.

Exploring the codebase

We’ll dive into the flutter codebase that demonstrates the implementation of object detection in an AR gaming scenario. Here’s a breakdown of the key components:

class ARGameView extends StatefulWidget {
  ARGameView({
    Key? key,
    required this.title,
    required this.onDetectedObject,
  }) : super(key: key);
  final String title;
  final Function(DetectedObject) onDetectedObject;

  @override
  State<ARGameView> createState() => _ARGameViewState();
}

Stage Management

The _ARGameViewState class manages the state of the ARGameView widget. It initializes the object detector and other necessary variables in the initState method.

class _ARGameViewState extends State<ARGameView> {
  ObjectDetector? _objectDetector;
  DetectionMode _mode = DetectionMode.stream;
  bool _canProcess = false;
  bool _isBusy = false;
  CustomPaint? _customPaint;
  String? _text;
  var _cameraLensDirection = CameraLensDirection.back;
  int _option = 0;
  final _options = {
    'default': '',
    'object_custom': 'object_labeler.tflite',
  };
  
  @override
  void initState() {
    super.initState();
    _initializeDetector();
  }

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Detector Initialization

This code initializes an object detector using an existing machine learning model from Google ML Kit. objectDetector in this code snippet is a component of Google ML Kit’s computer vision capabilities, allowing developers to integrate powerful object detection and classification functionalities into their application with ease.  In the following block, it’s used for identifying and localizing objects within images or video frames. The model is capable of detecting multiple objects simultaneously and, optionally, classifying them based on predefined categories. 
void _initializeDetector() async {
    _objectDetector?.close();
    _objectDetector = null;

    if (_option == 0) {
      final options = ObjectDetectorOptions(
        mode: _mode,
        classifyObjects: true,
        multipleObjects: true,
      );
      _objectDetector = GoogleMlKit.vision.objectDetector(options);
    } else if (_option > 0 && _option <= _options.length) {
      final option = _options[_options.keys.toList()[_option]] ?? '';
      final modelPath = await getAssetPath('assets/ml/$option');
      final options = LocalObjectDetectorOptions(
        mode: _mode,
        modelPath: modelPath,
        classifyObjects: true,
        multipleObjects: true,
      );
      _objectDetector = GoogleMlKit.vision.objectDetector(options);
    }

    _canProcess = true;
}

Image Processing

The _processImage method employs the image recognition technique to analyze the captured image and detect objects using the initialized detector. Once objects are detected, the UI is updated accordingly using the _updateUI method.

Future<void> _processImage(InputImage inputImage) async {
    if (_objectDetector == null) return;
    if (!_canProcess) return;
    if (_isBusy) return;
    _isBusy = true;
    setState(() {
      _text = '';
    });
    final objects = await _objectDetector!.processImage(inputImage);
    _updateUI(objects);
    _isBusy = false;
    if (mounted) {
      setState(() {});
    }
}

UI Update

The _updateUI method updates the UI with the detected objects. If objects are detected, it displays the number of objects detected along with a visual representation of the objects using the CustomPaint widget. Otherwise, it displays a message indicating that no objects were detected.

void _updateUI(List<DetectedObject> objects) {
    if (objects.isNotEmpty) {
      setState(() {
        _text = 'Objects Detected: ${objects.length}';
        _customPaint = CustomPaint(
          painter: ObjectDetectPainter(objects),
        );
      });
    } else {
      setState(() {
        _text = 'No Objects Detected';
        _customPaint = null;
      });
    }
}

Full source code

class ARGameView extends StatefulWidget {  

ARGameView({
    Key? key,
    required this.title,
    required this.onDetectedObject,
  }) : super(key: key);

  final String title;
  final Function(DetectedObject) onDetectedObject;

  @override
  State<ARGameView> createState() => _ARGameViewState();
}

class _ARGameViewState extends State<ARGameView> {
  ObjectDetector? _objectDetector;
  DetectionMode _mode = DetectionMode.stream;
  bool _canProcess = false;
  bool _isBusy = false;
  CustomPaint? _customPaint;
  String? _text;
  var _cameraLensDirection = CameraLensDirection.back;
  int _option = 0;
  final _options = {
    ‘default’: ,
    ‘object_custom’: ‘object_labeler.tflite’,
  };

  @override
  void initState() {
    super.initState();
    _initializeDetector();
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: Text(widget.title),
      ),
      body: Stack(
        children: [
          DetectorView(
            title: ‘AR Game Detector’,
            customPaint: _customPaint,
            text: _text,
            onImage: _processImage,
            initialCameraLensDirection: _cameraLensDirection,
            onCameraLensDirectionChanged: (value) =>
                _cameraLensDirection = value,
            onCameraFeedReady: _initializeDetector,
            initialDetectionMode: DetectorViewMode.values[_mode.index],
            onDetectorViewModeChanged: _onScreenModeChanged,
          ),
          Positioned(
            top: 30,
            left: 100,
            right: 100,
            child: Row(
              children: [
                Spacer(),
                Container(
                  decoration: BoxDecoration(
                    color: Colors.black54,
                    borderRadius: BorderRadius.circular(10.0),
                  ),
                  child: Padding(
                    padding: const EdgeInsets.all(4.0),
                    child: _buildDropdown(),
                  ),
                ),
                Spacer(),
              ],
            ),
          ),
        ],
      ),
    );
  }

  Widget _buildDropdown() => DropdownButton<int>(
        value: _option,
        icon: const Icon(Icons.arrow_downward),
        elevation: 16,
        style: const TextStyle(color: Colors.blue),
        underline: Container(
          height: 2,
          color: Colors.blue,
        ),
        onChanged: (int? option) {
          if (option != null) {
            setState(() {
              _option = option;
              _initializeDetector();
            });
          }
        },
        items: List<int>.generate(_options.length, (i) => i)
            .map<DropdownMenuItem<int>>((option) {
          return DropdownMenuItem<int>(
            value: option,
            child: Text(_options.keys.toList()[option]),
          );
        }).toList(),
      );

  void _onScreenModeChanged(DetectorViewMode mode) {
    switch (mode) {
      case DetectorViewMode.gallery:
        _mode = DetectionMode.single;
        _initializeDetector();
        return;
      case DetectorViewMode.liveFeed:
        _mode = DetectionMode.stream;
        _initializeDetector();
        return;
    }
  }

  void _initializeDetector() async {
    _objectDetector?.close();
    _objectDetector = null;

    if (_option == 0) {
      final options = ObjectDetectorOptions(
        mode: _mode,
        classifyObjects: true,
        multipleObjects: true,
      );
      _objectDetector = GoogleMlKit.vision.objectDetector(options);
    } else if (_option > 0 && _option <= _options.length) {
      final option = _options[_options.keys.toList()[_option]] ?? ;
      final modelPath = await getAssetPath(‘assets/ml/$option’);
      final options = LocalObjectDetectorOptions(
        mode: _mode,
        modelPath: modelPath,
        classifyObjects: true,
        multipleObjects: true,
      );
      _objectDetector = GoogleMlKit.vision.objectDetector(options);
    }

    _canProcess = true;
  }

  Future<void> _processImage(InputImage inputImage) async {
    if (_objectDetector == null) return;
    if (!_canProcess) return;
    if (_isBusy) return;
    _isBusy = true;
    setState(() {
      _text = ;
    });
    final objects = await _objectDetector!.processImage(inputImage);
    _updateUI(objects);
    _isBusy = false;
    if (mounted) {
      setState(() {});
    }
  }

  void _updateUI(List<DetectedObject> objects) {
    if (objects.isNotEmpty) {
      // Update UI with detected objects
      setState(() {
        _text = ‘Objects Detected: ${objects.length}’;
        _customPaint = CustomPaint(
          painter: ObjectDetectPainter(objects),
        );
      });
    } else {
      setState(() {
        _text = ‘No Objects Detected’;
        _customPaint = null;
      });
    }
  }
}

Use cases in Gaming

Integrating object detection in Mobile App Development for game development opens up a plethora of use cases and gameplay possibilities, leveraging Machine learning and Google ML Kit:

  1. 1. Augmented Reality Games: Players can immerse themselves in virtual adventures overlaid onto their surroundings, engaging in treasure hunts, creature hunts, or virtual battles, thus fostering collaboration and competition within the flutter community.
  2.  
  3. 2. Object Recognition Challenges: Games can challenge players to identify and interact with real-world objects to unlock rewards, solve puzzles, or progress through levels, enhancing engagement and interactivity. This integration can be facilitated through a dedicated GitHub repository for easy access and collaboration among developers.
  4.  
  5. 3. Immersive Storytelling: Object detection can enrich storytelling in games by triggering events or narrative elements based on real-world objects detected by the camera, offering personalized and interactive experiences for players, thus pushing the boundaries of mobile gaming experiences.
  6.  
  7. 4. Multiplayer AR Experiences: Friends can collaborate or compete in multiplayer AR games, working together or against each other to achieve objectives or complete challenges within shared virtual environments, fostering social interaction and engagement in the gaming community.

Conclusion

Object detection technology is revolutionizing the gaming industry, enabling developers to create immersive augmented reality experiences that blur the lines between the virtual and real worlds. By exploring the codebase and understanding its implementation, we’ve gained insight into how object detection can be leveraged to build innovative and engaging gaming experiences. As AR gaming continues to evolve, the possibilities for creative gameplay and storytelling are endless, promising exciting adventures for players to explore.
Vaishakhi Panchmatia

As the Tech Co-Founder at Yugensys, I’m driven by a deep belief that technology is most powerful when it creates real, measurable impact.
At Yugensys, I lead our efforts in engineering intelligence into every layer of software development — from concept to code, and from data to decision.
With a focus on AI-driven innovation, product engineering, and digital transformation, my work revolves around helping global enterprises and startups accelerate growth through technology that truly performs.
Over the years, I’ve had the privilege of building and scaling teams that don’t just develop products — they craft solutions with purpose, precision, and performance.Our mission is simple yet bold: to turn ideas into intelligent systems that shape the future.
If you’re looking to extend your engineering capabilities or explore how AI and modern software architecture can amplify your business outcomes, let’s connect.At Yugensys, we build technology that doesn’t just adapt to change — it drives it.

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