Roads　are　the　foundation　of　intelligent　transportation　systems　(ITS),　yet　cracks　are　widely　present　in　roads　and　seriously　affect　system　performance.　Cracks　not　repaired　promptly　can　develop　into　severe　road　defects,　significantly　increasing　the　risk　of　traffic　accidents.　Researchers　in　the　community　have　started　to　focus　on　the　automatic　sensing　of　cracks　in　asphalt　pavements,　while　it　is　still　a　challenging　task　on　concrete　pavements.　Cracks　in　the　concrete　pavement　are　easily　recognized　as　interrupted　segments　rather　than　a　continuous　whole　due　to　the　interference　of　the　surface　texture.　This　mistake　can　seriously　mislead　the　judgment　of　cracks　and　subsequent　road　repair.　In　this　article,　we　aim　to　solve　the　challenge　by　enhancing　contextual　information　about　cracks　within　the　images.　We　first　extract　the　information　from　the　local　and　global　representations　using　image　information　and　then　fuse　it　into　complete　contextual　information　by　a　designed　multilayer　perceptron　(MLP).　Finally,　we　use　the　discriminative　loss　to　constrain　the　edges　of　cracks　and　backgrounds　using　complete　crack　contextual　information.　We　have　collected　and　annotated　several　images　of　concrete　pavements　from　several　significant　provinces　in　China.　Experiments　show　that　our　method　achieves　the　best　performance　compared　to　state-of-the-art　methods,　especially　in　edge　determination.