Detecting　the　components　in　aerial　images　is　a　crucial　task　in　automatic　visual　inspection　for　power　lines.　Currently,　deep　learning　models　guided　by　external　knowledge　have　achieved　promising　performances　compared　to　directly　applying　the　benchmark　detectors.　However,　the　component　relationship,　as　human　commonsense　knowledge　for　object　reasoning,　is　rarely　investigated　in　this　field.　This　study　presents　a　graph-based　relation　guided　network　for　power　line　component　detection,　which　exploits　correlations　of　regions,　images,　and　categories.　The　visual　relation　module　is　employed　to　learn　region-to-region　relationship　and　enhance　the　visual　features　of　each　proposal　that　may　contain　components.　Meanwhile,　two　guidance　modules　are　proposed　to　capture　image-to-region　correlation　and　distinctively　facilitate　the　category　classification　and　position　regression,　which　has　not　been　considered　in　previous　methods.　Moreover,　the　category　graphs　built　in　these　two　modules　are　able　to　explore　category-to-category　dependencies　that　can　further　promote　the　network　ability.　Experimental　results　demonstrate　that　the　proposed　method　can　achieve　more　accurate　and　reasonable　component　detection　compared　to　previous　methods,　which　verifies　the　effectiveness　of　the　proposed　model　incorporated　with　relation　knowledge.