Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

作     者：E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman 

作者机构：Department of Computer SciencesCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent MachinesFaculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information TechnologyCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control EngineeringFaculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department Electronics and Electrical CommunicationsFaculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and CommunicationsFaculty of EngineeringZagazig UniversityZagazig44519Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：1845-1861页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Research Funding Program(Grant No#FRP-1440-23) 

主　　题：Deep learning fuzzy logic image fusion IR images 

摘      要：Image fusion has become one of the interesting fields that attract researchers to integrate information from different image *** is involved in several *** of the recent applications is the robotic *** application necessitates image enhancement of both infrared(IR)and visible *** paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep *** basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured ***,an enhancement model is carried out on the fused image to increase its *** image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art *** results prove that the fuzzy logic enhancement gives the best results from the image quality ***,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.