Aiming　at　the　detection　of　loose　parts　in　nuclear　power　plant　under　low　signal-to-noise　ratio　(SNR)　environment,　an　impulse　response　extraction　method　for　loose　parts　based　on　blind　deconvolution　algorithm　is　proposed　to　reduce　the　false　alarm　rate　and　the　false　negative　rate.　Combined　with　Support　Vector　Machine　classification　identification　algorithm,　a　detection　method　for　loose　parts　of　nuclear　power　plant　is　developed　as　well　in　low　SNR　environment.　The　simulatedstudies　are　carried　out　based　on　the　test　data　from　steel　ball-flat　plate　drop　experiment　superimposed　with　real　background　noise,　and　the　parameters　involved　in　the　blind　deconvolution　algorithm　are　optimized.　The　results　show　that　the　optimized　blind　deconvolution　algorithm　can　effectively　suppress　the　noise.　Consequently,　the　impulse　response　signal　with　SNR　as　low　as　-20dB　can　be　recovered　well　and　the　noise　energy　is　reduced　by　75%.　The　proposed　detection　method　for　loose　parts　has　a　very　low　false　negative　rate　and　a　zero　false　alarm　rate　for　noise　and　pulse　interference　even　when　the　signal　to　noise　ratio　is　as　low　as　-14　dB.　These　results　indicate　the　presented　loose　parts　detection　method　has　a　good　ability　of　anti-false　positive　and　false　negative.　©　2020,　Editorial　Department　of　JVMD.　All　right　reserved.