Preprocessing Data For Object Detection in Computer vision

This post is about preprocessing steps performed over image data while solving object detection problems using computer vision.

Let’s begin by selecting right data in computer vision for object detection. when selecting the best images for object detection in computer vision, you want to choose images that provide the most value in terms of training a robust and accurate model. Here are some factors to consider when selecting the best images:

Object Coverage: Choose images that have good object coverage, meaning the objects of interest are well-represented and visible in the image. Images with objects that are occluded, heavily overlapped, or partially cut off may provide less valuable training data.

Object Variation: Aim for images that exhibit variation in terms of object appearance, pose, scale, lighting conditions, and backgrounds. The selected images should cover a wide range of scenarios to ensure the model learns to generalize well.

Image Quality: Prefer images with good quality and clarity. Blurry, noisy, or low-resolution images may negatively impact the model’s ability to detect objects accurately.

Annotation Accuracy: Check the accuracy and quality of the annotation in the images. Images with precise and accurate bounding box…