Here are three practical computer vision projects that developers can build to strengthen their skills and create useful applications:
1. Object Detection and Tracking System A strong starting project is building an object detection and tracking system. Using frameworks like YOLO (You Only Look Once) or pre-trained models from TensorFlow or PyTorch, you can detect objects in images or videos and track their movements. For example, you could create a traffic monitoring tool that counts vehicles in a video feed or tracks pedestrians in a crowded area. Implementing this requires understanding bounding box predictions, non-max suppression to filter overlapping detections, and integrating tracking algorithms like SORT (Simple Online and Realtime Tracking) to follow objects across frames. Tools like OpenCV can help visualize results by drawing bounding boxes and labels. This project teaches core concepts like model inference, post-processing, and real-time performance optimization.
2. Image Segmentation for Medical Diagnostics Image segmentation, which classifies each pixel in an image, is widely used in medical imaging. A project could involve training a U-Net or Mask R-CNN model to segment tumors in MRI scans or identify infected regions in X-rays. For example, the Kaggle Lung Segmentation challenge provides CT scan datasets to practice segmenting lung tissues. You’ll need to preprocess data (e.g., normalize pixel values), handle class imbalances (e.g., rare tumor pixels), and evaluate results using metrics like Dice coefficient. Frameworks like PyTorch Lightning simplify training loops, while libraries like MONAI offer medical imaging-specific tools. This project highlights challenges like working with limited labeled data and applying domain-specific optimizations.
3. GAN-Based Image-to-Image Translation Generative Adversarial Networks (GANs) can transform images from one domain to another, such as converting sketches to photos or enhancing low-resolution images. A classic example is using Pix2Pix to turn satellite images into maps or CycleGAN to apply artistic styles to photos. For instance, you could train a model to convert daytime street scenes to nighttime using the Cityscapes dataset. This involves setting up generator and discriminator networks, tuning loss functions (e.g., adversarial loss, cycle consistency loss), and managing training stability. Tools like TensorFlow’s Keras GAN library provide templates for experimentation. This project deepens understanding of generative models, domain adaptation, and balancing computational resources during training.
Each project addresses distinct computer vision challenges while offering hands-on experience with industry-standard tools and datasets. By focusing on real-world problems, developers can build portfolio pieces that demonstrate both technical skill and practical relevance.