Digital　imaging　and　image-processing　techniques　have　revolutionized　the　way　of　power　line　inspection　in　recent　years.　Massive　images　are　captured　and　utilized　for　further　processing　to　maintain　the　reliability,　safety,　and　sustainability　of　power　transmission.　For　power　line　inspection,　the　component　region　in　the　delivered　images　is　required　to　be　centered,　large,　and　clear　enough.　In　this　paper,　a　component-oriented　image　quality　assessment　method　is　proposed　to　automatically　predict　image　quality　according　to　the　demand　of　power　line　inspection.　The　proposed　method　considers　two　factors:　spatial　characteristic　evaluation　and　sharpness　evaluation.　The　spatial　characteristic　evaluation　utilizes　YOLOv3　to　evaluate　whether　the　component　region　is　sufficiently　centered　and　large,　which　enables　the　observer　to　quickly　find　the　target.　The　sharpness　evaluation　employs　ResNet　to　evaluate　the　clarity　of　component　and　makes　the　condition　monitoring　more　accurately.　For　final　quality　assessment,　a　multi-stage　filtering　strategy　is　presented　to　aggregate　these　two　factors　and　obtain　high　quality　inspection　images.　The　experimental　results　indicate　that　the　high-quality　images　can　be　accurately　identified　to　satisfy　the　requirements　of　power　line　inspection.　The　proposed　quality　assessment　method　enhances　the　efficiency　for　further　data　analysis　of　aerial　images.