The　detection　of　fetal　ultrasound　standard　planes　(FUSPs)　is　important　for　the　diagnosis　of　fetal　malformation　and　the　prevention　of　perinatal　death.　As　a　promising　deep-learning　technique　in　FUSP　detection,　SonoNet＇s　network　parameters　have　a　large　size.　In　this　paper,　we　introduced　a　light　pyramid　convolution　(LPC)　block　into　SonoNet　and　proposed　LPC-SonoNet　with　reduced　network　parameters　for　FUSP　detection.　The　LPC　block　used　pyramid　convolution　architecture　inspired　by　SimSPPF　from　YOLOv6　and　was　able　to　extract　features　from　various　scales　with　a　small　parameter　size.　Using　SonoNet64　as　the　backbone,　the　proposed　network　removed　one　of　the　convolutional　blocks　in　SonoNet64　and　replaced　the　others　with　LPC　blocks.　The　proposed　LPC-SonoNet　model　was　trained　and　tested　on　a　publicly　available　dataset　with　12,400　ultrasound　images.　The　dataset　with　six　categories　was　further　divided　into　nine　categories.　The　images　were　randomly　divided　into　a　training　set,　a　validation　set,　and　a　test　set　in　a　ratio　of　8:1:1.　Data　augmentation　was　conducted　on　the　training　set　to　address　the　data　imbalance　issue.　In　the　classification　of　six　categories　and　nine　categories,　LPC-SonoNet　obtained　the　accuracy　of　97.0%　and　91.9%　on　the　test　set,　respectively,　slightly　higher　than　the　accuracy　of　96.60%　and　91.70%　by　SonoNet64.　Compared　with　SonoNet64　with　14.9　million　parameters,　LPC-SonoNet　had　a　much　smaller　parameter　size　(4.3　million).　This　study　pioneered　the　deep-learning　classification　of　nine　categories　of　FUSPs.　The　proposed　LPC-SonoNet　may　be　used　as　a　lightweight　network　for　FUSP　detection.