<p>Through the computing advance and GPU (Graphics Processing
Unit) availability for math calculation, the deep learning field becomes more
popular and prevalent. Object detection with deep learning, which is the part
of image processing, plays an important role in automatic vehicle drive and
computer vision. Object detection includes object localization and object
classification. Object localization involves that the computer looks through
the image and gives the correct coordinates to localize the object. Object
classification is that the computer classification targets into different
categories. The traditional image object detection pipeline idea is from
Fast/Faster R-CNN [32] [58]. The region proposal network
generates the contained objects areas and put them into classifier. The first
step is the object localization while the second step is the object
classification. The time cost for this pipeline function is not efficient.
Aiming to address this problem, You Only Look Once (YOLO) [4] network is born. YOLO is the
single neural network end-to-end pipeline with the image processing speed being
45 frames per second in real time for network prediction. In this thesis, the
convolution neural networks are introduced, including the state of art
convolutional neural networks in recently years. YOLO implementation details
are illustrated step by step. We adopt the YOLO network for our applications
since the YOLO network has the faster convergence rate in training and provides
high accuracy and it is the end to end architecture, which makes networks easy
to optimize and train. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/11340089 |
| Date | 10 December 2019 |
| Creators | Haoyu Shi (8100614) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/OBJECT_DETECTION_IN_DEEP_LEARNING/11340089 |
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