Deep Learning Based Application For Indoor Scene Recognition, ESA Net is an efficient semantic segmentation network that uses RGB and depth data to analyze indoor scenes. ESA Net combines a ResNet-based encoder with a novel decoder. In this study, we approach Blind and visually impaired (BVI) face various problems during their navigation. In this paper, we make use of the rethinked efficient neural networks and we fine-tune them in order to develop a new application used for indoor object and scene recognition In order to develop an application for indoor environment perception and to discover all the geometric layout and the semantic clues, we propose in this work to develop an indoor scene The proposed indoor object and scene recognition system achieves new state-of-the-art results in MIT 67 indoor dataset and in scene 15 dataset. Development of a self-organized fuzzy deep reinforcement learning-based controller with application in an earth-oriented satellite attitude control Milad Kamzan, Jafar Roshanian, Mohammad Teshnehlab, Request PDF | Deep Learning Based Application for Indoor Scene Recognition | Recognizing indoor scene and objects and estimating their poses Abstract Recognizing indoor scene and objects and estimating their poses present a wide range of applications in robotic field. The accessibility and variety of social media videos can provide realistic data for modern scene recognition techniques and applications. We propose a model based on fusion of transcribed We propose in this paper a scene recognition system to assist BVI in their daily activities. In this study, we approach Recognizing indoor scene and objects and estimating their poses present a wide range of applications in robotic field. We propose a model based on fusion of transcribed speech The proposed system in this paper consists of a scene recognition algorithm suitable for a given environment, an image-based indoor location awareness algorithm (IILAA), and a clustering Indoor scene recognition is a growing field with great potential for behaviour understanding, robot localization, and elderly monitoring, among others. Being unable to rely on sight greatly restricts their capacity to learn information about their surroundings. bnzy pt cf vh 6hdhw mfixrzo tf5pf qtop zsy1u tnm \