Are there any significant challenges to object detection?

Are there any significant challenges to object detection?

The limited amount of annotated data currently available for object detection proves to be another substantial hurdle. Object detection datasets typically contain ground truth examples for about a dozen to a hundred classes of objects, while image classification datasets can include upwards of 100,000 classes.

What’s the difference between image classification and object detection?

Image classification involves predicting the class of one object in an image. Object localization refers to identifying the location of one or more objects in an image and drawing abounding box around their extent. Object detection combines these two tasks and localizes and classifies one or more objects in an image.

What’s the difference between object recognition and object localization?

Object localization refers to identifying the location of one or more objects in an image and drawing abounding box around their extent. Object detection combines these two tasks and localizes and classifies one or more objects in an image. When a user or practitioner refers to “ object recognition “, they often mean “ object detection “.

How are object detection algorithms used in real life?

Practitioners leverage several techniques to ensure detection algorithms are able to capture objects at multiple scales and views. Instead of selective search, Faster R-CNN’s updated region proposal network uses a small sliding window across the image’s convolutional feature map to generate candidate RoIs.

What are the dual priorities of object detection?

Dual priorities: object classification and localization The first major complication of object detection is its added goal: not only do we want to classify image objects but also to determine the objects’ positions, generally referred to as the object localization task.

Can a single shot detector detect small objects?

Despite the precaution of using multiple feature maps, single-shot detectors notoriously struggle to detect small objects, especially those in tight groupings like a flock of birds. Feature maps from multiple CNN layers help predict objects at multiple scales.