To　resolve　the　problems　of　deep　convolutional　neural　network　models　with　many　parameters　and　high　memory　resource　consumption,　a　lightweight　network-based　algorithm　for　building　detection　of　Minnan　folk　light　synthetic　aperture　radar　(SAR)　images　is　proposed.　Firstly,　based　on　the　rotating　target　detection　algorithm　R-centernet,　the　Ghost　ResNet　network　is　constructed　to　reduce　the　number　of　model　parameters　by　replacing　the　traditional　convolution　in　the　backbone　network　with　Ghost　convolution.　Secondly,　a　channel　attention　module　integrating　width　and　height　information　is　proposed　to　enhance　the　network＇s　ability　to　accurately　locate　salient　regions　in　folk　light　images.　Content-aware　reassembly　of　features　(CARAFE)　up-sampling　is　used　to　replace　the　deconvolution　module　in　the　network　to　fully　incorporate　feature　map　information　during　up-sampling　to　improve　target　detection.　Finally,　the　constructed　dataset　of　rotated　and　annotated　light　and　shadow　SAR　images　is　trained　and　tested　using　the　improved　R-centernet　algorithm.　The　experimental　results　show　that　the　improved　algorithm　improves　the　accuracy　by　3.8%,　the　recall　by　1.2%　and　the　detection　speed　by　12　frames/second　compared　with　the　original　R-centernet　algorithm.