The　arbitrary-shape　scene　text　detection　faces　enormous　challenges　in　accuracy　and　speed　with　increasing　demand　in　the　industrial　sector.　Although　most　studies　have　introduced　segmentation　and　achieved　remarkable　performance　in　text　detection　tasks,　researchers　ignored　the　importance　of　detection　efficiency.　This　paper　proposes　a　new　Joint　Multi-Space　Perception　Network　(JMNET)　for　efficient　scene　text　detection　to　address　this　issue.　Based　on　a　lightweight　feature　extraction　backbone,　we　put　forward　two　novel　modules,　i.e.,　Scale　Spatial　Perception　Module　(SSPM)　and　Attention　Spatial　Perception　Module　(ASPM)　to　enhance　the　expression　ability　of　text　features　with　low　computational　complexity.　Moreover,　we　propose　an　Unsupervised　Embedding　Spatial　Perception　Loss　function　(UnESP　Loss)　by　introducing　the　Euclidean　distance　measurement　between　the　embeddings　to　overcome　the　ambiguity　of　text　instance　boundary,　such　as　a　small　line　spacing.　In　this　way,　text　embedding　learning　is　not　restricted　by　specific　shapes,　and　detection　robustness　can　be　improved.　Extensive　experiments　on　four　benchmarks,　including　the　ICDAR2015,　MSRA-TD500,　CTW1500　and　Totaltext,　show　that　the　proposed　JMNET　achieves　competitive　performance　in　terms　of　both　accuracy　and　speed　over　the　state-of-the-art　methods.　Particularly,　our　method　can　reach　a　remarkable　F-measure　of　85.5%　at　52　FPS　on　Totaltext.　Code　is　available　at:https://github.com/sakura-910/JMNET.　©　2022　Elsevier　B.V.