Nucleic　acid　testing　is　currently　the　golden　reference　for　coronaviruses　(SARS-CoV-2)　detection,　while　the　SARS-CoV-2　antigen-detection　rapid　diagnostic　tests　(RDT)　is　an　important　adjunct.　RDT　can　be　widely　used　in　the　community　or　regional　screening　management　as　self-test　tools　and　may　need　to　be　verified　by　healthcare　authorities.　However,　manual　verification　of　RDT　results　is　a　time-consuming　task,　and　existing　object　detection　algorithms　usually　suffer　from　high　model　complexity　and　computational　effort,　making　them　difficult　to　deploy.　We　propose　LightR-YOLOv5,　a　compact　rotating　SARS-CoV-2　antigen-detection　RDT　results　detector.　Firstly,　we　employ　an　extremely　light-weight　L-ShuffleNetV2　network　as　a　feature　extraction　network　with　a　slight　reduction　in　recognition　accuracy.　Secondly,　we　combine　semantic　and　texture　features　in　different　layers　by　judiciously　combining　and　employing　GSConv,　depth-wise　convolution,　and　other　modules,　and　further　employ　the　NAM　attention　to　locate　the　RDT　result　detection　region.　Furthermore,　we　propose　a　new　data　augmentation　approach,　Single-Copy-Paste,　for　increasing　data　samples　for　the　specific　task　of　RDT　result　detection　while　achieving　a　small　improvement　in　model　accuracy.　Compared　with　some　mainstream　rotating　object　detection　networks,　the　model　size　of　our　LightR-YOLOv5　is　only　2.03MB,　and　it　is　12.6%,　6.4%,　and　7.3%　higher　in　mAP@.5:.95　metrics　compared　to　RetianNet,　FCOS,　and　R3Det,　respectively.