Endoscopic　thyroid　surgery　is　a　common　treatment　method　for　thyroid　diseases.　However,　due　to　the　small　size　of　parathyroid　gland,　its　unstable　position　during　surgery,　and　the　similar　color　and　texture　of　the　parathyroid　gland　to　its　surrounding　tissues　and　organs,　parathyroid　glands　are　extremely　difficult　to　be　visualized　and　detected　during　the　operation,　leading　to　accidental　damages　which　result　in　abnormal　parathyroid　hormone　secretion　after　surgery.　Therefore,　detecting　the　parathyroid　glands　and　preventing　damage　during　endoscopic　thyroid　surgery　is　critical.　The　current　target　detection　technology　using　SOAT　algorithm　can　help　detecting　the　small　parathyroid　area,　but　the　accuracy　and　efficiency　are　often　unsatisfied　because　of　the　high　image　noise.　In　this　research,　we　propose　a　PGNet　model　by　applying　an　anti-noise　feature　extraction　module　to　prevent　the　sharp　drop　in　the　accuracy　of　the　model　under　the　noisy　environment.　The　anti-noise　feature　extraction　module　is　based　on　residual　structure,　and　hence　the　capability　of　parathyroid　shape　detection　can　be　significantly　improved.　To　further　improve　the　detection　accuracy　of　the　parathyroid　gland,　an　ellipse　anchor　frame　and　an　improved　IoU　loss　function　have　been　applied　in　this　PGNet　model.　The　detection　accuracy　of　PGNet　model　has　been　verified　using　data　obtained　in　real-time　surgical　scenes,　and　the　results　show　that　the　PGNet　model　can　achieve　89.3%　parathyroid　detection　accuracy　when　the　IoU　threshold　is　0.5,　which　is　better　than　the　current　SOTA　CNN　model.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.