In　visual　simultaneous　localization　and　mapping　(SLAM)　systems,　traditional　methods　often　excel　due　to　rigid　environmental　assumptions,　but　face　challenges　in　dynamic　environments.　To　address　this,　learning-based　approaches　have　been　introduced,　but　their　expensive　computing　costs　hinder　real-time　performance,　especially　on　embedded　mobile　platforms.　In　this　article,　we　propose　a　robust　and　real-time　visual　SLAM　method　towards　dynamic　environments　using　acceleration　of　feature　extraction　and　object　detection　(AFO-SLAM).　First,　AFO-SLAM　employs　an　independent　object　detection　thread　that　utilizes　YOLOv5　to　extract　semantic　information　and　identify　the　bounding　boxes　of　moving　objects.　To　preserve　the　background　points　within　these　boxes,　depth　information　is　utilized　to　segment　target　foreground　and　background　with　only　a　single　frame,　with　the　points　of　the　foreground　area　considered　as　dynamic　points　and　then　rejected.　To　optimize　performance,　CUDA　program　accelerates　feature　extraction　preceding　point　removal.　Finally,　extensive　evaluations　are　performed　on　both　TUM　RGB-D　dataset　and　real　scenes　using　a　low-power　embedded　platform.　Experimental　results　demonstrate　that　AFO-SLAM　offers　a　balance　between　accuracy　and　real-time　performance　on　embedded　platforms,　and　enables　the　generation　of　dense　point　cloud　maps　in　dynamic　scenarios.