During　the　non-contact　geomagnetic　detection　of　pipeline　defects,　measured　signals　generally　contain　noise,　which　reduces　detection　efficiency.　Complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN)　has　recently　emerged　as　a　signal　filtering　method,　but　its　filtering　performance　is　influenced　by　two　parameters:　the　amplitude　of　added　noise　and　the　number　of　ensemble　trials.　To　solve　this　issue　and　improve　detection　accuracy　and　distinguishability,　a　detection　method　based　on　improved　CEEMDAN　(ICEEDMAN)　and　the　Teager　energy　operator　(TEO)　is　proposed.　The　magnetic　detection　signal　was　first　decomposed　into　a　series　of　intrinsic　mode　functions　(IMFs)　by　CEEMDAN　with　initial　parameters.　Signal　IMFs　were　then　distinguished　using　the　Hurst　exponent　to　reconstruct　the　preliminary　filtered　signal,　and　its　maximum　value　(except　the　zero　point)　of　the　normalized　autocorrelation　function　was　defined　as　salp　swarm　algorithm　(SSA)　fitness.　The　optimal　parameters　that　maximize　fitness　were　found　by　SSA　iterations,　and　their　corresponding　filtered　signal　was　obtained.　Finally,　the　gradient　calculation　and　TEO　were　carried　out　to　complete　non-contact　geomagnetic　detection.　The　results　of　the　simulated　signal　based　on　magnetic　dipole　under　a　noisy　environment　and　field　testing　prove　that　ICEEMDAN　denoising　has　better　filtering　performance　than　conventional　CEEMDAN　denoising　methods,　and　ICEEMDAN-TEO　has　obvious　advantages　compared　to　other　detection　methods　in　the　aspects　of　location　error,　peak　side-lobe　ratio,　and　integrated　side-lobe　ratio.