Barcode　detection　is　a　key　step　before　decoding　so　that　achieving　a　fast　and　accurate　detection　algorithm　is　of　significant　importance.　In　the　present　study,　we　propose　to　guide　the　pruning　of　channels　and　shortcut　layers　in　YOLOv4　through　sparse　training　to　obtain　the　compressed　model　ThinYOLOv4　for　barcode　detection.　Then　a　binary　classification　network　is　established　to　remove　the　prediction　boxes　that　do　not　contain　a　barcode,　thereby　obtaining　a　fast　and　accurate　barcode　detection　model.　In　order　to　evaluate　the　performance　of　the　proposed　method,　a　barcode　dataset　consisting　of　16,545　images　is　provided.　This　dataset　contains　common　types　of　barcodes　in　the　market　and　covers　different　practical　scenarios.　Furthermore,　interference　factors　such　as　blur,　low-contrast　are　considered　in　the　dataset　purposefully.　Obtained　results　show　that　the　proposed　method　achieves　a　recall　rate　of　93.8%　on　the　provided　dataset,　Meanwhile,　parameters　of　YOLOv4　are　reduced　from　63,943,071　to　400,649,　and　the　model　size　is　reduced　from　250,037 KB　to　1,587 KB,　while　the　corresponding　detection　speed　is　increased　to　260%　of　YOLOv4.　When　the　experiment　is　performed　on　the　1050Ti　GPU,　a　detection　speed　of　23.308 ms/image　is　achieved.　©　2021,　Springer　Nature　Singapore　Pte　Ltd.