Edge　detection　is　a　long　standing　but　still　challenging　problem.　Although　there　are　many　effective　edge　detectors,　none　of　them　can　obtain　ideal　edges　in　every　situation.　To　make　the　results　robust　for　any　image,　we　propose　a　new　edge　detection　algorithm　based　on　a　two-level　fusion　model　that　combines　several　typical　edge　detectors　together　with　new　proposed　edge　estimation　strategies.　At　the　first　level,　we　select　three　typical　but　diverse　edge　detectors.　The　edge　score　is　calculated　for　every　pixel　in　the　image　based　on　a　consensus　measurement　by　counting　positive　voting　number　of　approaches.　Then　results　are　combined　at　the　second　level　using　the　Hadamard　product　with　two　additional　edge　estimations　proposed　in　the　paper,　based　on　edge　spatial　characteristics,　where　one　is　binary　matrix　of　the　most　probable　edge　distribution　and　the　other　is　a　score　matrix　based　on　calculating　differences　between　maxima　and　minima　neighboring　intensity　change　at　each　point.　Comprehensive　experiments　are　conducted　on　two　image　databases,　and　three　evaluation　methods　are　employed　to　measure　the　performance,　viz.　F1-measure,　ROC　and　PFOM.　Experiments　results　show　that　our　proposed　method　outperforms　the　three　standard　baseline　edge　detectors　and　shows　better　results　than　a　state-of-the-art　method.