Object Detection in Images: How to Build an Object Detector in Navigator

Object detection enables technology to "see" and interpret images as humans do. The potential impact on the enterprise level is significant. With the right object detection model, businesses can address complex and unique tasks. 

Popular object detection algorithms can’t always deliver the real-time processing and specialized decisioning you need. In this article, we will teach readers how to create their own custom object detector using Navigator, a user-friendly AI tool from webAI. 

What is Object Detection in Images?

Object detection is a computer vision technique that utilizes neural networks to identify and classify objects within images and videos. This form of artificial intelligence trains computers to perceive the world like humans do. The models learn to recognize and label objects based on categories, identifying what objects are where within an image. 

The process involves object classification and object localization. 

• Object classification is a technique that determines to which category a detected object belongs. 
• Object localization involves marking these objects using a bounding box, unlike image segmentation, which marks objects at the pixel level. 

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Object detection models are crucial in various applications, including augmented reality, home security, and personal projects like organizing photos. 

Preparing to Build Your Object Detector

Accomplishing object detection tasks requires some prep work. 

• Set Your Goal: Define exactly what your object detection task will do. 
• Understand the Basics: Gather the following tools/data. 
  
  • A dataset of images or videos for training the model.
  • A tool to annotate your dataset. 
  • A platform to train and deploy the model to identify objects (like Navigator).
• Gathering Your Dataset: Collect data from a mix of sources using diverse images or videos for better accuracy. Ensure each input image or video fits the requirements of your chosen annotation tool (e.g., supported file types). 

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Building an Object Detector in Navigator

Creating a vision detection model from scratch is an exciting process, but requires an understanding of object detection methods and training steps. Building usable, helpful object detectors is an exhaustive process that will adhere to the following steps. 

This guide will help you build an object detector using webAI’s Navigator.  

Before you begin, if you have not done so already, download Navigator here.

Step 1: Prepare the Video/Images and Set Up Annotations

Annotation involves identifying and labeling the exact objects you want the computer to detect. The process provides the data the computer needs to recognize objects accurately.

1. Select Clips or Images: For this example, we’ll use a video of a single person walking down an empty street. When training an actual model, your collection of videos/images must be diverse and include types of images and clips of different types of objects in different situations.
2. Use an Annotation Tool: Open an annotation tool like the Computer Vision Annotation Tool (CVAT). 
3. Create a New Task:
   
   • Name your project (e.g., "Person Detection").
   • Define the object classification (e.g., "Person").
   • Assign a color for annotations and confirm your setup.
     
     1. Note: In this step, you must label every object you’re training your model to recognize.
4. Upload the Videos: Drag and drop your video files into the tool, then wait for it to process. 

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Step 2: Annotate the Video

1. Open the Annotation Workspace: Navigate through the video frames or images to find the object in each frame/image (e.g., the person).
2. Create Annotations
   
   • If using CVAT, use the rectangle tool to draw a bounding box around the object (person).
   • Select the "track" option to help interpolate annotations across frames.
   • Adjust bounding boxes every 10 frames or so for accuracy.
3. Review and Save: Track through all frames to check that they’re properly annotated and save your progress.

Note: You can annotate frame by frame, but it's a tedious process, and using the "track" option does this for you accurately. 

Step 3: Export the Dataset

1. Export Annotations 
   
   • If using CVAT, go to the task actions and select "Export Dataset." If using another annotation tool, find their export dataset function. 
   • Choose the COCO 1.0 format, save the images, and name your dataset.
   • Save the file and wait for the export to complete (this may take some time).
2. Download the Dataset: Once ready, download the dataset.

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Step 4: Prepare the Dataset for Training

You need to prepare your dataset before uploading it to webAI. 

1. Unzip and Rename
   
   • Unzip the downloaded ZIP file.
   • Find the annotations folder, open it, rename the annotations file to annotations.json, and move it to the main directory. 
   • Delete the annotations folder.
2. Organize Images
   
   • Find the images folder, open it, and select all images from the nested folder. These images are each frame of your video.
   • Move all images to the main images folder directory.
   • Delete the nested folder.
3. Double Check Folder Structure
   
   • Your folder should now include the annotations.json file and the images folder containing each image.
4. Repeat: Repeat this process until your entire dataset is named and ordered correctly. 

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Step 5: Train the Model in webAI Navigator

1. Open webAI: Select a blank canvas, open Elements (bottom left), and add the Trainer element to the canvas.
2. Set Up the Trainer
   
   • Name your element.
   • Click the data training path and select the folder containing your annotations.json and images.
   • Define an output directory for the trained model.
3. Modify other settings as desired: You own the models you train in webAI. Adjust settings to form a custom model that functions as you need it. 
4. Run Training: Click Run to start training and monitor progress via the training metrics. The training process will take some time. 

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Step 6: Test the Model

1. Create a New Project:
   
   • Name it (e.g., "Person Vision Model").
   • Add the following elements to the Navigator canvas:
     
     • Media Loader: Load your video file and set the frame rate.
     • Output Viewer: Display detection results.
     • Object Detector Inference: Link to your trained model.
2. Connect Elements
   
   • Link the media loader, detector, and output viewer.
   • Load the trained model artifact and configure detection settings.
     
     • For example, you’ll set the frame rate (30 frames per second for true speed)
3. Run the Model: Play the video (select Run) and observe the detected objects highlighted in real time.

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Tips for Optimizing Your Object Detector

Working with a diverse dataset is the best way to improve accuracy across various applications. After you’ve finished training your annotated dataset, use the tips below to optimize your detector.  

1. Test the model on unseen data to evaluate performance. 
2. Adjust training parameters or annotations for better results.
3. Deploy the model in a controlled environment and collect feedback on its performance.

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Common Challenges and How to Solve Them

Depending on your unique goals, you may face various challenges that require creative problem-solving to address. 

• Low Detection Accuracy: Add more training data that includes objects with various backgrounds, deformations, occlusions, and lighting variations.
• Overfitting: Overfitting occurs when a machine learning model memorizes the training data rather than learning general patterns. Multiple strategies can fix this issue, including using a more diverse data set and using data augmentation techniques (e.g., rotations, scaling, lighting adjustments) to simulate new scenarios. 
• Hardware Limitations: You could try optimizing the model for lower computational requirements or using Navigator’s features to streamline processing.

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How Navigator Makes Object Detection Easy

Navigator stands apart from other object detection methods when it comes to its user-friendly interface, pre-build tools, and flexible deployment. The drag-and-drop functionality simplifies the entire process. 

This webAI tool provides everything you need to train and deploy models without extensive coding knowledge. With Navigator, you can create and deploy your own model locally and integrate them into personal projects seamlessly. The AI applications are near-endless. 

Models designed with webAI can run on existing hardware and devices like smartphones or local servers, eliminating the need for cloud-based infrastructure and allowing you to own your model. 

Build Your Vision with Object Detection

Building an object detector allows for custom systems that can make specialized decisioning. With tools like Navigator, it’s easier than ever to get started, regardless of experience level.

Sign up and explore Navigator—an exciting AI application that puts you in control of your own data. 

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