Autonomous-Driving-Car-Detection

Problem Statement

You are working on a self-driving car. As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds while you drive around.

You've gathered all these images into a folder and have labelled them by drawing bounding boxes around every car you found.

Tasks:

• Use object detection on a car detection dataset
• Deal with bounding boxes

Mode

"You Only Look Once" (YOLO) is a popular algorithm because it achieves high accuracy while also being able to run in real-time. This algorithm "only looks once" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes.

Summary for YOLO:

• Input image (608, 608, 3)
• The input image goes through a CNN, resulting in a (19,19,5,85) dimensional output.
• After flattening the last two dimensions, the output is a volume of shape (19, 19, 425):
  • Each cell in a 19x19 grid over the input image gives 425 numbers.
  • 425 = 5 x 85 because each cell contains predictions for 5 boxes, corresponding to 5 anchor boxes, as seen in lecture.
  • 85 = 5 + 80 where 5 is because $(p_c, b_x, b_y, b_h, b_w)$ has 5 numbers, and 80 is the number of classes we'd like to detect
• You then select only few boxes based on:
  • Score-thresholding: throw away boxes that have detected a class with a score less than the threshold
  • Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes
• This gives you YOLO's final output.

References: The ideas presented in this notebook came primarily from the two YOLO papers. The implementation here also took significant inspiration and used many components from Allan Zelener's GitHub repository. The pre-trained weights used in this exercise came from the official YOLO website.

• Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi - You Only Look Once: Unified, Real-Time Object Detection (2015)
• Joseph Redmon, Ali Farhadi - YOLO9000: Better, Faster, Stronger (2016)
• Allan Zelener - YAD2K: Yet Another Darknet 2 Keras
• The official YOLO website (https://pjreddie.com/darknet/yolo/)