As　the　high　voltage　circuit　breaker　works,　the　metal　parts　are　prone　to　many　types　of　mechanical　defects,　such　as　plastic　deformation,　metal　loss,　corrosion,　and　so　on.　The　defects　will　inevitably　decrease　the　performance,　mechanical　reliability,　lifetime,　and　other　critical　parameters　of　the　breaker.　Therefore,　the　detection　of　mechanical　defects　is　of　great　help　to　reduce　the　mechanical　failure　rate　of　circuit　breakers.　In　the　present　paper,　a　novel　method　based　on　edge　detection　and　deep　learning　is　proposed　to　detect　the　mechanical　defects　of　the　high　voltage　circuit　breaker.　Firstly,　the　high　resolution　images　of　the　circuit　breaker　are　photographed,　and　the　components　in　the　images　are　segmented　in　the　minimum　range　by　the　contour　detection　algorithm.　After　binarization　and　morphological　processing　of　the　segmented　images,　the　edge　is　drawn　with　the　improved　edge　detection　algorithm.　However,　the　components　of　the　circuit　breaker　are　complicated,　which　results　that　the　edge　features　of　the　critical　component　are　difficult　to　extract.　Then,　the　specific　contour　of　components　is　detected　by　the　depth　estimation　and　segmentation　methods.　Secondly,　a　deep　learning　platform　base　on　Tensorflow　is　established,　which　is　an　optimization　method　based　on　Convolutional　Neural　Network.　In　order　to　enhance　the　feature　extraction　capacity,　a　convolution　kernel　is　inputted　into　a　sparse　self-coding　network　to　proceeds　with　the　optimal　pre-training.　Based　on　the　above　method,　the　features　of　images　are　automatically　learned,　and　each　convolution　carries　for　the　equilibrium　of　image　entropy　of　convolution　kernel　by　introducing　similarity　constraint　rule.　Finally,　3　types　of　mechanical　defects,　including　plastic　deformation,　metal　loss,　corrosion,　images　and　non-defect　images　as　input　are　exploited　to　validate　the　CNN　with　convolution　kernel　optimized.　The　experimental　results　show　that　the　average　accuracy　of　the　approach　is　better　than　the　traditional　convolution　neural　network　model　and　has　good　feature　extraction　ability　and　generalization　ability.　In　conclusion,　combined　with　the　improved　edge　detection　and　deep　learning　algorithms,　the　detection　of　typical　mechanical　defects　can　be　well　performed.　For　the　collected　images　of　circuit　breaker,　so　as　to　reduce　the　impact　of　noise　on　defect　accuracy　of　mechanical,　the　proposed　method　provides　a　reference　for　the　non-destructive　mechanical　defect　detection　of　high　voltage　circuit　breaker.　©　2022　IEEE.