How to Design Object Detectors in MATLAB for Computer Vision University Assignment

Sarah Mitchell

United States

Prof. Sarah Mitchell – Computer Science faculty at Massachusetts Institute of Technology (MIT), USA. She has extensive experience in deep learning, autonomous systems, and MATLAB-based computer vision projects.

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We can broadly classify object detectors into three main categories:

1. Classical Computer Vision-Based Detectors
   These detectors rely on traditional computer vision techniques such as image segmentation, feature detection, and pattern recognition. They use image properties like edges, shapes, colors, and textures to identify objects. Common examples include color thresholding, blob analysis, histogram-based approaches, and feature descriptors like SIFT and SURF. Classical techniques are computationally efficient and useful when working with small datasets or when deep learning resources are limited.
2. Machine Learning-Based Detectors
   Machine learning approaches combine traditional image features with algorithms that can learn patterns from data. For example, the Aggregate Channel Features (ACF) detector uses precomputed image features and trains a classifier to recognize objects of interest. Another example is the Cascade Object Detector, which is based on the Viola-Jones framework. Machine learning detectors are effective for problems where labeled datasets are available, but training a full deep learning model might be excessive.
3. Deep Learning-Based Detectors
   Deep learning approaches use neural networks to learn object features directly from labeled images. Convolutional neural networks (CNNs) form the backbone of modern deep learning detectors, allowing systems to automatically extract complex patterns. Popular deep learning detectors include YOLO (You Only Look Once), R-CNN, Fast R-CNN, and Faster R-CNN. These methods require larger datasets and more computational resources but are highly accurate and adaptable.

In this blog, we focus on machine learning-based detectors using MATLAB’s ACF detector as an example. However, the concepts can be extended to deep learning detectors with minimal modifications in workflow.

Generating Ground Truth Data

The first step in designing an object detector is creating a labeled dataset, commonly referred to as ground truth. Ground truth data consists of images annotated with object labels and the locations of objects within those images. Without accurate ground truth, the detector cannot learn to recognize objects reliably.

Labeled images typically contain information such as the type of object (e.g., buoy, pedestrian, vehicle) and the region of interest within the image. While there are many publicly available datasets, specific projects often require custom datasets, especially for niche or novel applications.

MATLAB provides a powerful tool called the Ground Truth Labeler, which allows you to create and manage labeled datasets. Using this tool, you can annotate rectangular regions, polygons, pixel areas, or entire scenes. It also supports automation, where you can use built-in algorithms or integrate your own code to speed up labeling. Once labeling is complete, MATLAB enables you to export the dataset as a ground truth object that can be used for training detectors.

Generating accurate ground truth data is a crucial step. The quality of your labels directly impacts the performance of your object detector. Even a perfectly designed detector will fail if the training data is inconsistent or inaccurate.

Training Object Detectors in MATLAB

Once the ground truth dataset is ready, the next step is training the object detector. MATLAB’s Computer Vision Toolbox provides built-in functions for training both machine learning and deep learning detectors. In the case of the ACF detector, the function trainACFObjectDetector is used to train the model using labeled data.

The training workflow involves feeding the ground truth images into the detector function along with training options. The function analyzes the features in the images and builds a model that can detect objects in unseen images. For deep learning detectors, functions like trainRCNNObjectDetector, trainFastRCNNObjectDetector, and trainFasterRCNNObjectDetector can be used with specific training parameters.

An important consideration during training is the training configuration. This includes selecting the number of iterations, the size of the training data, and other hyperparameters that influence the accuracy and speed of the detector. MATLAB’s training functions provide flexibility, allowing students to experiment with different settings and observe how changes impact performance.

After training, the detector object can be used to identify objects in new images using the detect method. This allows you to visually confirm whether the detector correctly identifies objects of interest.

Evaluating Object Detectors

Training a detector is only part of the process. Evaluating its performance is equally important. MATLAB offers functions to evaluate detectors using metrics such as precision, miss rate, and confusion matrices. These metrics help quantify how well the detector performs on unseen data.

One key parameter in evaluation is the overlap threshold, which determines how closely the detected bounding box must align with the ground truth bounding box. A higher threshold demands more accurate detection, while a lower threshold allows for some flexibility. Choosing the right threshold depends on the specific application and the desired level of detection accuracy.

It’s important to evaluate the detector using a dataset that is independent of the training data. This ensures that the detector is not overfitting to the specific images it was trained on and can generalize to new scenarios. MATLAB provides a range of visualization tools that help students analyze detector performance, such as plotting detected bounding boxes and comparing them with ground truth.

Deploying Object Detectors to Embedded Systems

Once the detector is trained and evaluated, the final step is deployment. Many robotics and autonomous systems use low-level programming languages like C or C++ for real-time operation. MATLAB supports code generation, which allows students to convert their detector code into C/C++ for deployment on embedded systems.

For the ACF detector, MATLAB requires packaging the detector within a function and converting the detector object into a structure that can be stored in a MAT-file. This structure is then loaded within the function and used to perform detection. Using persistent variables ensures that the detector does not need to be reconstructed every time the function is called, improving efficiency.

MATLAB’s MATLAB Coder app simplifies the process of generating deployable code. Students can follow a guided workflow to convert MATLAB functions into optimized C/C++ code that runs on hardware platforms like embedded computers or microcontrollers. This capability allows MATLAB-trained detectors to transition seamlessly from research and development to practical application in robotics and autonomous systems.

Key Considerations for University Projects

• Start with a clear problem definition: Understand the objects you need to detect and the environment in which detection will occur. This informs your choice of features, dataset, and detector type.
• Invest time in ground truth data: Accurate and representative labeling is critical. Spend time ensuring the dataset covers different lighting conditions, object orientations, and backgrounds.
• Experiment with detector types: Machine learning detectors are often simpler and faster to train, but deep learning detectors provide higher accuracy for complex tasks. Evaluate the trade-offs based on project requirements.
• Use independent test data: Always evaluate detectors on images that were not part of the training dataset to ensure generalization.
• Iterate and optimize: Detection performance can often be improved by refining the dataset, adjusting training parameters, or incorporating more advanced features.

Benefits of Using MATLAB for Object Detection

1. Integrated environment: MATLAB provides tools for data preprocessing, training, evaluation, and deployment all in one environment.
2. User-friendly labeling: The Ground Truth Labeler simplifies the tedious task of creating labeled datasets.
3. Extensive tutorials and documentation: MATLAB’s documentation and online tutorials allow students to quickly learn best practices and advanced techniques.
4. Seamless code generation: Students can convert MATLAB detectors into deployable C/C++ code, bridging the gap between research and real-world applications.

These advantages make MATLAB an ideal platform for university-level assignments and research projects in computer vision and robotics.

Real-World Applications

The techniques discussed here have broad applications. In autonomous vehicles, object detectors identify pedestrians, traffic signs, and other vehicles. In robotics competitions, detectors help robots navigate and interact with objects in their environment. Even in industrial automation, object detectors can be used for quality control, assembly line monitoring, and safety systems. Learning to design detectors in MATLAB equips students with skills that are highly relevant to both research and industry.

Conclusion

Designing an object detector in MATLAB involves a systematic approach that starts with understanding the problem, generating ground truth data, training the detector, evaluating its performance, and deploying it for real-world use. By following the workflows provided in MATLAB and leveraging its tools for machine learning and deep learning, students can develop robust detectors efficiently. This hands-on experience not only enhances understanding of computer vision concepts but also provides practical skills that are directly applicable to projects, competitions, and future careers in robotics and AI.

Whether you are a beginner exploring computer vision or an advanced student preparing for a robotics competition, MATLAB offers a versatile platform for designing object detectors. With accurate datasets, thoughtful design, and careful evaluation, students can create detectors that perform reliably and are ready for deployment in real-world applications.