Fault　diagnosis　of　insulators　in　aerial　images　is　an　essential　task　of　power　line　inspection　to　maintain　the　reliability,　safety,　and　sustainability　of　power　transmission.　This　paper　develops　a　novel　method　for　intelligent　diagnosis　of　electrical　insulators　based　on　deep　learning,　termed　Box-Point　Detector,　which　consists　of　a　deep　convolutional　neural　network　followed　by　two　parallel　branches　of　convolutional　heads.　These　two　branches　are　utilized　to　locate　the　fault　region　and　estimate　insulator　endpoints,　which　presents　a　new　representation　for　insulator　faults.　Endpoints　of　the　faulty　insulator　string　can　provide　detailed　and　correlative　information　for　enhancing　the　diagnosis　capability　of　component-dependent　faults　that　occur　on　component　bodies.　The　proposed　Box-Point　Detector　implements　all　predictions　including　region　and　endpoint　into　one　network　thus　forms　an　efficient　end-to-end　structure,　and　adopts　a　smaller　downsampling　ratio　to　generate　high　resolution　feature-maps　in　order　to　preserve　more　original　information　for　small　size　faults.　Experimental　results　indicate　that　Box-Point　Detector　can　accurately　diagnose　high-voltage　insulator　faults　in　real-time　under　various　conditions.　Compared　with　some　previous　works　using　Faster　R-CNN,　SSD,　and　cascading　network,　our　Box-Point　Detector　shows　more　competitively　capabilities　with　high　accuracy　and　robustness.