Training R-CNN-based custom object detectors
R-CNN stands for region-based convolutional neural network. Region-based within R-CNN refers to the region proposals used to identify objects within an image. Note that R-CNN assists in identifying both the objects present in the image and their location within it.
In the following sections, we will learn about the working details of R-CNN before training it on our custom dataset.
Working details of R-CNN
Let’s get an idea of R-CNN-based object detection at a high level using the following diagram:
Figure 7.9: Sequence of steps for R-CNN (image source: https://arxiv.org/pdf/1311.2524.pdf)
We perform the following steps when leveraging the R-CNN technique for object detection:
- Extract region proposals from an image. We need to ensure that we extract a high number of proposals to not miss out on any potential object within the image.
- Resize (warp) all the extracted regions to get regions of...
