An　enhanced　approach　for　detecting　defects　in　Printed　Circuit　Boards　(PCBs)　using　a　significantly　improved　version　of　the　YOLOv8　algorithm　is　proposed　in　this　research,　the　proposed　method　is　referred　to　as　YOLOv8-TDD　(You　Only　Look　Once　Version8-Targeted　Defect　Detection).　This　novel　approach　integrates　cutting-edge　components　such　as　Swin　Transformers,　Dynamic　Snake　Convolution　(DySnakeConv),　and　Biformer　within　the　YOLOv8　architecture,　aiming　to　address　and　overcome　the　limitations　associated　with　traditional　PCB　inspection　methods.　The　YOLOv8-TDD　adaptation　incorporates　Swin　Transformers　to　leverage　hierarchical　feature　processing　with　shifted　windows,　enhancing　the　model＇s　efficiency　and　capability　in　capturing　complex　image　details.　Dynamic　Snake　Convolution　is　implemented　to　dynamically　adapt　filter　responses　based　on　the　input　feature　maps,　offering　tailored　feature　extraction　that　is　highly　responsive　to　the　varied　textures　and　defects　in　PCBs.　The　Biformer,　with　bidirectional　processing　capability,　enriches　the　model＇s　contextual　understanding,　providing　a　comprehensive　analysis　of　the　PCB　images　to　pinpoint　defects　more　accurately.　Experimental　results　demonstrate　that　YOLOv8-TDD　model,　achieves　a　precision　of　97.9%,　a　mean　Average　Precision　(mAP0.5)　of　95.71%.　This　enhanced　model　offers　significant　potential　for　practical　applications　in　PCB　manufacturing,　promising　to　elevate　quality　control　standards　through　more　reliable　defect　detection.