Open AccessImprovising Weakly Supervised Object Detection WSOD using Deep Learning Technique
Author(s) -
Jyoti G. Wadmare,
Sunita Patil
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3796.029320
Subject(s) - object detection , artificial intelligence , computer science , object (grammar) , computer vision , image (mathematics) , improvisation , viola–jones object detection framework , deep learning , detector , object class detection , supervised learning , cognitive neuroscience of visual object recognition , pattern recognition (psychology) , artificial neural network , face detection , art , telecommunications , facial recognition system , visual arts
Object detection is closely related with video and image analysis. Under computer vision technology, object detection model training with image-level labels only is challenging research area.Researchers have not yet discovered accurate model for Weakly Supervised Object Detection (WSOD). WSOD is used for detecting and localizing the objects under the supervision of image level annotations only.The proposed work usesself-paced approach which is applied on region proposal network of Faster R-CNN architecture which gives better solution from previous weakly-supervised object detectors and it can be applied for computer visionapplications in near future.