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The output of this network is combined with the feature map and passed in
through a similar pipeline to the Fast R-CNN network, as shown in Figure 7. The
Faster R-CNN network is about 10x faster than the Fast R-CNN network, making
it approximately 250x faster than an R-CNN network:
Chapter 5
Figure 7: Faster R-CNN network architecture
Another somewhat different class of object detection networks are Single Shot
Detectors (SSD) such as You Only Look Once (YOLO). In these cases, each image is
split into a predefined number of parts using a grid. In the case of YOLO, a 7×7 grid
is used, resulting in 49 subimages. A predetermined set of crops with different aspect
ratios are applied to each sub-image. Given B bounding boxes and C object classes,
the output for each image is a vector of size (7 * 7 * (5B + C)). Each bounding box has
a confidence and coordinates (x, y, w, h), and each grid has prediction probabilities
for the different objects detected within them.
The YOLO network is a CNN that does this transformation. The final predictions
and bounding boxes are found by aggregating the findings from this vector. In
YOLO a single convolutional network predicts the bounding boxes and the related
class probabilities. YOLO is the faster solution for object detection, but the algorithm
might fail to detect smaller objects (an implementation can be found at https://
www.kaggle.com/aruchomu/yolo-v3-object-detection-in-tensorflow).
Instance segmentation
Instance segmentation is similar to semantic segmentation – the process of
associating each pixel of an image with a class label – with a few important
distinctions. First, it needs to distinguish between different instances of the same
class in an image. Second, it is not required to label every single pixel in the
image. In some respects, instance segmentation is also similar to object detection,
except that instead of bounding boxes, we want to find a binary mask that covers
each object.
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