Yolo Real Time Object Detection

International Journal of Computer Trends and Technology (IJCTT)          
© 2020 by IJCTT Journal
Volume-68 Issue-6
Year of Publication : 2020
Authors : Sujata Chaudhari, Nisha Malkan, Ayesha Momin, Mohan Bonde


MLA Style: Sujata Chaudhari, Nisha Malkan, Ayesha Momin, Mohan Bonde  "Yolo Real Time Object Detection" International Journal of Computer Trends and Technology 68.6 (2020):70-76.

APA Style Sujata Chaudhari, Nisha Malkan, Ayesha Momin, Mohan Bonde. Yolo Real Time Object Detection.  International Journal of Computer Trends and Technology, 68(6),70-76.

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.

[1] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[2] L. Han, “Object detection module based on implementation of Java and OpenCV,” Journal of Computer Applications, vol. 28, no. 3, pp. 773775, Oct. 2008.
[3] P.Hemalatha, C.K.Hemantha Lakshmi and Dr.S.A.K.Jilani, "Real time Image Processing based Robotic Arm Control Standalone System using Raspberry pi" SSRG International Journal of Electronics and Communication Engineering 2(8), 2015
[4] Figure 2f from: Irimia R, Gottschling M (2016) “Taxonomic revision of Rochefor- tia Sw. (Ehretiaceae, Boraginales)”. Data Journal 4: e7720. https://doi.org/10.3897/BDJ.4.e7720.
[5] “GitHub-experiencor/keras-yolo2: Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported”. A YOLO demo to detect raccoon run entirely in browser is accessible at https://git.io/vF7vI (not on windows)Retrieved from https://github.com/experiencor/keras-yolo2 YOLO Object Detection with OpenCV and Python. Retrieved from https://www.arunponnusa-object-detection-opencvpython. html
[6] Sagar Badgujar, Amol Mahalpure, Priyaka Satam, Dipalee Thakar, Prof. Swati jaiswal, "Real time number plate recognition and tracking vehicle system" SSRG International Journal of Computer Science and Engineering 2(12), 2015
[7] Retrieved from https://towardsdatascience.com/r-cnn-fast-rcnn- faster-r-cnn-yolo-object-detectionalgorithmsBiodiversity- 36d53571365e
[8] Retrieved from https://heartbeat.fritz.ai/gentle-guide-onhow- yolo-object-localization-works-with-keras-part-2- 65fe59ac12d