143 research outputs found
A Survey of Deep Learning-Based Object Detection
Object detection is one of the most important and challenging branches of
computer vision, which has been widely applied in peoples life, such as
monitoring security, autonomous driving and so on, with the purpose of locating
instances of semantic objects of a certain class. With the rapid development of
deep learning networks for detection tasks, the performance of object detectors
has been greatly improved. In order to understand the main development status
of object detection pipeline, thoroughly and deeply, in this survey, we first
analyze the methods of existing typical detection models and describe the
benchmark datasets. Afterwards and primarily, we provide a comprehensive
overview of a variety of object detection methods in a systematic manner,
covering the one-stage and two-stage detectors. Moreover, we list the
traditional and new applications. Some representative branches of object
detection are analyzed as well. Finally, we discuss the architecture of
exploiting these object detection methods to build an effective and efficient
system and point out a set of development trends to better follow the
state-of-the-art algorithms and further research.Comment: 30 pages,12 figure
An Approach Of Features Extraction And Heatmaps Generation Based Upon Cnns And 3D Object Models
The rapid advancements in artificial intelligence have enabled recent progress of self-driving vehicles. However, the dependence on 3D object models and their annotations collected and owned by individual companies has become a major problem for the development of new algorithms. This thesis proposes an approach of directly using graphics models created from open-source datasets as the virtual representation of real-world objects. This approach uses Machine Learning techniques to extract 3D feature points and to create annotations from graphics models for the recognition of dynamic objects, such as cars, and for the verification of stationary and variable objects, such as buildings and trees. Moreover, it generates heat maps for the elimination of stationary/variable objects in real-time images before working on the recognition of dynamic objects. The proposed approach helps to bridge the gap between the virtual and physical worlds and to facilitate the development of new algorithms for self-driving vehicles
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