Open AccessEvaluation and Evolution of Object Detection Techniques YOLO and R-CNN
Author(s) -
Krishna Dixit,
Mahima Chadaga,
Sinchana Savalgimath,
G Ragavendra Rakshith,
Naveen Kumar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1154.0782s319
Subject(s) - convolutional neural network , object detection , computer science , artificial intelligence , generalization , object (grammar) , task (project management) , computer vision , pattern recognition (psychology) , detector , deep learning , mathematics , telecommunications , engineering , mathematical analysis , systems engineering
Object detection has boomed in areas like image processing in accordance with the unparalleled development of CNN (Convolutional Neural Networks) over the last decade. The CNN family which includes R-CNN has advanced to much faster versions like Fast-RCNN which have mean average precision(Map) of up to 76.4 but their frames per second(fps) still remain between 5 to 18 and that is comparatively moderate to problem-solving time. Therefore, there is an urgent need to increase speed in the advancements of object detection. In accordance with the broad initiation of CNN and its features, this paper discusses YOLO (You only look once), a strong representative of CNN which comes up with an entirely different method of interpreting the task of detecting the objects. YOLO has attained fast speeds with fps of 155 and map of about 78.6, thereby surpassing the performances of other CNN versions appreciably. Furthermore, in comparison with the latest advancements, YOLOv2 attains an outstanding trade-off between accuracy and speed and also as a detector possessing powerful generalization capabilities of representing an entire image