YOLO based detection is a real time
object detection system. It looks at the whole image at test time so its predictions
are informed by global context in the image. It also makes predictions with a single
The YOLO target detection network is an end-to-end, one-step structure which was
developed in recent years . By learning from a large number of labeled images, the
detected target bounding box (BB) and the categorical
probability prediction can be directly obtained. It has a relatively fast and high
mean average precision(mAP) performance to scale variations, since it adapts
multiple convolution layers for multi-scale object detection.
this unify the separate components of object detection into a single neural network.
This network uses features from the entire image to predict each bounding box. It
also predicts all bounding boxes for an image simultane-ously.
This means the network reasons globally about the full image and all the objects in
the image.It is capable of real-time speeds while maintaining high average
The image feeded into the model via webcam or cctv camera is divided into
13 * 13 grids
Each of these cells is responsible for predicting 5 bounding boxes. A
bounding box describes the rectangle that encloses an object.YOLO also outputs a
confidence score that tells us how certain it is that the
predicted bounding box actually encloses some object. This score doesn’t say
anything about what kind of object is in the box, just if the shape of the box is
any good. For each bounding box, the cell also predicts
a class. This works just like a classifier: it gives a probability distribution over
all the possible classes. The confidence score for the bounding box and the class
prediction are combined into one final score that
tells us the probability that this bounding box contains weapon or not. Since there
are 13×13 = 169 grid cells and each cell predicts 5 bounding boxes, we end up with
845 bounding boxes in total. It turns out that most.
of these boxes will have very low confidence scores, so we only keep the boxes whose
final score is 30% or more