Accurate　detection　of　weeds　is　a　key　technology　for　developing　automated　weeding　equipment.　To　address　the　problems　of　high　detection　complexity　and　poor　robustness　resulting　from　the　complex　distribution　and　variety　of　weeds,　we　proposed　a　weed　detection　approach　for　vegetable　seedling　based　on　the　improved　YOLOv5　algorithm　and　image　processing,　implemented　on　a　self-developed　mobile　robot　platform.　The　weed　detection　complexity　was　reduced　by　indirectly　detecting　weeds　through　identifying　vegetables,　thus　improving　the　detection　accuracy　and　robustness.　The　convolutional　block　attention　module　(CBAM)　attention　module　was　added　to　the　backbone　feature　extraction　network　of　the　YOLOv5　object　detection　algorithm　to　enhance　the　focus　of　the　network　on　vegetable　targets,　and　the　Transformer　module　was　added　to　enhance　the　global　information　capture　capability.　The　results　showed　that　the　average　detection　accuracy　of　the　improved　YOLOv5　algorithm　for　vegetable　targets　could　reach　95.7%,　which　was　increased　by　5.8%,　6.9%,　10.3%,　13.1%,　9.0%,　5.2%,　and　3.2%　compared　with　Faster　R-CNN,　SSD,　EfficientDet,　RetinaNet,　YOLOv3,　YOLOv4,　and　YOLOv5,　respectively.　The　average　detection　time　of　the　algorithm　for　a　single　run　was　11　ms,　indicating　good　real-time　performance.　The　method　defined　green　plants　outside　the　vegetable　border　as　weeds,　and　combined　the　extreme　green　(ExG)　with　the　OTSU　threshold　segmentation　method　to　segment　weeds　from　the　soil　background.　Finally,　the　weed　connectivity　domain　was　marked,　followed　by　outputting　the　weed　plasmids　and　detection　frames.　The　proposed　method　could　provide　a　technical　reference　for　automated　precision　weeding　in　agriculture.　©　2023,　Editorial　of　Board　of　Journal　of　Graphics.　All　rights　reserved.