Most　traditional　loop　closure　detection　(LCD)　methods　rely　on　manual　feature　design,　which　is　sensitive　to　environmental　conditions.　Convolutional　neural　networks　(CNNs)　cope　better　with　illumination　changes　by　extracting　hierarchical　features　and　ignoring　the　local　spatial　characteristics　of　images.　We　propose　an　LCD　algorithm　that　combines　VGG16,　NetVLAD,　and　image　pyramids　to　enhance　its　accuracy　and　robustness.　In　particular,　a　three-level　image　pyramid　was　constructed　via　downsampling,　and　then　a　feature　pyramid　(FP)　layer　was　obtained　by　extracting　features　through　VGG16　network　on　different　image　resolutions.　The　obtained　FPs　were　then　passed　into　the　VLAD　model,　and　this　model　outputted　VLAD　vectors　by　performing　residual　summation　with　L2　normalization.　Finally,　a　triplet　loss　function　was　employed　for　training.　Experimental　results　on　two　benchmark　datasets　and　a　real　scenario　dataset　demonstrated　that　this　algorithm　outperforms　the　NetVLAD　baseline　and　the　VGG16　network,　exhibiting　superior　feature-learning　capabilities　and　achieving　a　higher　LCD　accuracy.　Further,　it　maintained　real-time　performance　with　only　a　2%　increase　in　processing　time.　The　results　indicate　that　the　proposed　method　detects　loop　closures　even　in　complex　environments　with　varying　conditions　and　perspectives.　Hence,　the　approach　can　be　used　for　large-scale　visual　simultaneous　localization　and　mapping　applications,　such　as　autonomous　driving,　where　LCD　plays　a　crucial　role　in　mapping.