In　real　life,　doors　are　ubiquitous,　and　opening　doors　has　become　a　＇necessary　skill＇　for　general-purpose　robots.　The　key　for　robots　to　achieve　the　task　of　opening　doors　is　to　obtain　accurate　three-dimensional　spatial　coordinates　of　the　grip　point　of　the　door　handle.　The　commonly　used　binocular　camera　localization　technique　gets　the　target　depth　information　through　parallax,　but　there　are　defects　such　as　visual　blindness.　To　recognize　the　door　handle　in　real-time　and　predict　the　3D　coordinates　of　the　door　handle　grip　point,　this　paper　adopts　a　monocular　camera　localization　technique　combined　with　an　advanced　target　detection　algorithm　(yolov5)　to　design　a　monocular　vision-based　door　handle　grip　point　localization　network:　a　total　of　target　detection　module　and　target　localization　module　are　included.　The　target　detection　model　incorporates　BiFPN　feature　fusion　to　extract　effective　features　and　fuse　them　through　a　bidirectional　pyramid　structure;　the　SMOTE　overfitting　sampling　algorithm　is　incorporated　to　compensate　for　the　uneven　sample　distribution　by　generating　some　small　samples.　The　experimental　results　show　that　the　detection　accuracy　and　classification　accuracy　of　the　model　in　this　paper　are　higher　than　those　of　yolov5　and　other　models.　©　2023　IEEE.