The document discusses object detection using deep learning algorithms like deep neural networks, CNNs, R-CNN, Fast R-CNN, and YOLO. It explains that YOLO applies a single neural network to the full image to divide it into regions and predict bounding boxes and probabilities for each region in one pass, unlike prior methods that apply classifiers to multiple locations and scales. Compared to Fast R-CNN, YOLO performs detection by passing the image through a fully convolutional network once to output predictions for bounding boxes and class probabilities for each grid region.

1. MINI PROJECT WITH SEMINAR PPT S.Kausalya
Reg No:1951002

2. It is a state-of-the-art,
real-time object detection
system

8. TYPES OF DL ALGORITHMS
Deep Neural networks
CNN
R-CNN
Fast R-CNN
YOLO

10. HIDDEN LAYER IN NN

13. YOLO

15. How it works?
 Prior detection systems repurpose classifiers or
localizers to perform detection. They apply the model
to an image at multiple locations and scales. High
scoring regions of the image are considered
detections.
 We use a totally different approach. We apply a
single neural network to the full image. This network
divides the image into regions and predicts bounding
boxes and probabilities for each region. These
bounding boxes are weighted by the predicted
probabilities.

16. High level idea
Compared to other region proposal classification networks (fast RCNN) which
perform detection on various region proposals and thus end up performing
prediction multiple times for various regions in a image, Yolo architecture is more
like FCNN (fully convolutional neural network) and passes the image (nxn) once
through the FCNN and output is (mxm) prediction. This the architecture is splitting
the input image in mxm grid and for each grid generation 2 bounding boxes and
class probabilities for those bounding boxes. Note that bounding box is more
likely to be larger than the grid itself