For　intelligent　inspection　of　power　lines,　a　core　task　is　to　detect　components　in　aerial　images.　Currently,　deep　supervised　learning,　a　data-hungry　paradigm,　has　attracted　great　attention.　However,　considering　real-world　scenarios,　labeled　data　are　usually　limited,　and　the　utilization　of　abundant　unlabeled　data　is　rarely　investigated　in　this　field.　This　study　deploys　a　pretrained　model　for　power　line　component　detection　based　on　a　self-supervised　pretraining　approach,　which　exploits　useful　information　from　unannotated　data.　Concretely,　we　design　a　new　masking　strategy　based　on　the　structural　characteristic　of　power　lines　to　guide　the　pretraining　process　with　meaningful　semantic　content.　Meanwhile,　a　Siamese　architecture　is　proposed　to　extract　complete　global　features　by　using　dual　reconstruction　with　semantic　targets　provided　by　the　proposed　masking　strategy.　Then,　the　knowledge　distillation　is　utilized　to　enable　the　pretrained　model　to　learn　both　domain-specific　and　general　representations.　Moreover,　a　feature　pyramid　mechanism　is　adopted　to　capture　multiscale　features,　which　can　benefit　the　detection　task.　Experimental　results　show　that　the　proposed　approach　can　successfully　improve　the　performance　of　a　variety　of　detection　frameworks　for　power　line　components,　and　outperforms　other　self-supervised　pretraining　methods.