Aiming　at　the　problem　of　low　accuracy　of　the　method　for　solar　cell　defect　detection,　a　surface　defect　detection　algorithm　based　on　the　improved　YOLOv5s　solar　cell　is　proposed.　First,　in　order　to　solve　the　problem　of　small　target　defect　detection　on　the　cell　sheet,　the　Contextual　Transformer　Network　(CoT)　is　proposed,　which　can　provide　global　contextual　information　for　small　targets　and　the　model　better　at　predicting　small　targets.　Secondly,　by　adding　CBAM　attention　to　the　C3　module　in　the　Head　part,　the　important　channels　and　spatial　locations　of　the　input　feature　maps　can　be　better　captured　to　improve　the　performance　and　robustness　of　the　model.　Next,　the　integrity　of　feature　information　is　ensured　by　using　CARAFE,　a　lightweight　generalized　up-sampling　operator,　to　reduce　the　loss　of　feature　information　during　up-sampling.　Finally,　by　using　WIoU　as　the　bounding　box　loss　function,　the　accuracy　of　the　regression　can　be　greatly　improved　and　the　convergence　of　model　can　be　achieved　quickly.　The　experimental　results　show　that　compared　with　the　original　algorithm,　the　improved　YOLOv5s　improves　the　three　indicators　of　Precision,　Recall,　and　mAP@0.　5　by　5.　5%,　4.　1%,　and　3.　3%　respectively,　and　the　detection　speed　reaches　76　FPS,　which　meets　the　requirements　of　solar　cell　defect　detection.　©　2024,　Science　Press.　All　rights　reserved.