Computer Vision Engineer interview questions

Computer vision is a field of artificial intelligence focused on enabling machines to interpret and understand visual information from the world. Image processing, on the other hand, involves manipulating images to enhance them or extract information. The key difference is that computer vision aims to understand images at a deeper semantic level.

CNNs are designed to automatically and adaptively learn spatial hierarchies of features from images. They consist of layers with pooling and convolution operations that help in extracting and learning high-level features to perform tasks such as object detection and image classification.

In autonomous vehicles, computer vision is essential for perceiving and understanding the environment by processing data from cameras and other sensors to identify objects, track their movements, and make driving decisions.

Handling varying lighting conditions involves using techniques like data augmentation to make models robust to changes, applying normalization methods, and using advanced architectures like CNNs that can learn invariant features.

Annotation is crucial because it provides labeled data that trains computer vision models to understand what objects to identify and classify. Accurate annotation helps in improving model accuracy and performance.

Model evaluation in computer vision involves using metrics such as precision, recall, F1-score, and IoU to assess performance. Validation is done using separate validation datasets to ensure models generalize well to unseen data.

In a recent project, I implemented object detection using YOLOv3 to identify and localize multiple objects in images. I optimized the model for real-time performance using model compression techniques.

Feature extraction can be done using pre-trained CNNs like VGG, ResNet, or through custom-designed layers that allow the model to learn relevant features automatically during training.

Common datasets include ImageNet for image classification tasks, COCO for object detection, and Open Images Dataset for large-scale vision tasks.

Transfer learning involves taking a pre-trained model on a large dataset and fine-tuning it on a smaller, specific dataset. It is useful in computer vision to leverage existing knowledge and reduce training time, especially when limited data is available.