Yolo Real Time Object Detection
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Sujata Chaudhari, Nisha Malkan, Ayesha Momin, Mohan Bonde|
|DOI : 10.14445/22312803/IJCTT-V68I6P112|
How to Cite?
Sujata Chaudhari, Nisha Malkan, Ayesha Momin, Mohan Bonde, "Yolo Real Time Object Detection," International Journal of Computer Trends and Technology, vol. 68, no. 6, pp. 70-76, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I6P112
Now a day’s object detection has been a main topic in computer vision systems. With the knowledge of deep learning techniques, the accuracy of object detection has been improved. The project aims to include modern technique for object detection with the goal of achieving high accuracy with a real-time performance. An object detection system is dependent on other computer vision techniques for helping the deep learning-based approach, which leads to slow and non-desirable performance is a big challenge. In this project, we use the complete techniques of deep learning to solve the problem of object detection in an end-to-end fashion. The network is trained on available data-set (PASCAL VOC), on which an object detection challenge is conducted annually. The following system is fast and accurate, thus help those applications which require object detection. We present a new approach to object detection is YOLO Real Time. Earlier research on object detection re-purposed classifiers to perform detection.
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