Crowd　counting　provides　an　important　foundation　for　public　security　and　urban　management.　Due　to　the　existence　of　small　targets　and　large　density　variations　in　crowd　images,　crowd　counting　is　a　challenging　task.　Mainstream　methods　usually　apply　convolution　neural　networks　(CNNs)　to　regress　a　density　map,　which　requires　annotations　of　individual　persons　and　counts.　Weakly-supervised　methods　can　avoid　detailed　labeling　and　only　require　counts　as　annotations　of　images,　but　existing　methods　fail　to　achieve　satisfactory　performance　because　a　global　perspective　field　and　multi-level　information　are　usually　ignored.　We　propose　a　weakly-supervised　method,　DTCC,　which　effectively　combines　multi-level　dilated　convolution　and　transformer　methods　to　realize　end-to-end　crowd　counting.　Its　main　components　include　a　recursive　swin　transformer　and　a　multi-level　dilated　convolution　regression　head.　The　recursive　swin　transformer　combines　a　pyramid　visual　transformer　with　a　fine-tuned　recursive　pyramid　structure　to　capture　deep　multi-level　crowd　features,　including　global　features.　The　multi-level　dilated　convolution　regression　head　includes　multi-level　dilated　convolution　and　a　linear　regression　head　for　the　feature　extraction　module.　This　module　can　capture　both　low-　and　high-level　features　simultaneously　to　enhance　the　receptive　field.　In　addition,　two　regression　head　fusion　mechanisms　realize　dynamic　and　mean　fusion　counting.　Experiments　on　four　well-known　benchmark　crowd　counting　datasets　(UCF_CC_50,　ShanghaiTech,　UCF_QNRF,　and　JHU-Crowd++)　show　that　DTCC　achieves　results　superior　to　other　weakly-supervised　methods　and　comparable　to　fully-supervised　methods.