Nowadays,　the　automatic　detection　of　harmful　organisms　in　power　places　has　attracted　attention　due　to　the　extensive　unattended　way　of　power　places.　However,　surveillance　pictures　are　prone　to　motion　blurring　and　harmful　organisms　cannot　be　effectively　detected　due　to　their　frequent　and　fast　movements　in　power　places.　On　the　basis　of　the　improved　Cycle-Consistent　Adversarial　Networks　(CycleGAN)　model,　we　propose　a　method　for　removing　motion　blur　from　the　images　of　harmful　biological　organisms　in　power　places.　This　method　does　not　require　paired　blurred　and　real　sharp　images　for　training,　which　is　consistent　with　actual　requirements.　In　addition,　our　method　improves　the　classical　CycleGAN　model　by　combining　cycle　consistency　and　perceptual　loss　to　enhance　the　detail　authenticity　of　image　texture　restoration　and　improve　the　detection　accuracy.　The　model　uses　Wasserstein　GAN　with　gradient　penalty　(WGAN-GP)　as　a　loss　function　to　train　the　depth　model.　Given　the　existence　of　the　GAN　itself,　the　entire　real　image　distribution　space　is　difficult　to　fill　with　the　generated　image　distribution　space.　Experimental　results　show　that　the　proposed　method　effectively　improves　the　detection　accuracy　of　harmful　organisms　in　power　places.　©　2019　Association　for　Computing　Machinery.