Enhanced Object Detection and Classification via Multi-Method Fusion  

作　　者：Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park 

机构地区：[1]Department of Computer Science,Air University,Islamabad,44000,Pakistan [2]Department of Information Systems,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [3]Department of Computer Science,College of Computer Science and Information System,Najran University,Najran,55461,Saudi Arabia [4]Department of Computer Sciences,Faculty of Computing and Information Technology,Northern Border University,Rafha,91911,Saudi Arabia [5]Department of Computer Engineering,Tech University of Korea,237 Sangidaehak-ro,Siheung-si,Gyeonggi-do,15073,South Korea

出　　处：《Computers, Materials & Continua》2024年第5期3315-3331,共17页计算机、材料和连续体（英文）

基　　金：a grant from the Basic Science Research Program through the National Research Foundation(NRF)(2021R1F1A1063634)funded by the Ministry of Science and ICT(MSIT);Republic of Korea.This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R410);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding program Grant Code(NU/RG/SERC/12/6).

摘　　要：Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关 键 词：BRIEF features saliency map fuzzy c-means object detection object recognition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]