One　of　the　key　steps　in　pavement　maintenance　is　the　fast　and　accurate　identification　of　the　distresses,　defects,　and　pavement　markings　and　ability　to　conduct　the　maintenance　before　the　irreversible　damages.　Recently,　convolution　neural　network　(CNN)　has　emerged　as　a　powerful　tool　to　automatically　identify　the　pavement　cracks,　where　many　of　the　CNN　models　take　long　computation　time.　To　solve　the　problem,　an　adaptive　lightweight　model,　named　MobileCrack,　is　proposed　in　this　study.　MobileCrack　realizes　the　fast　computation　using　the　following　settings:　(1)　reduce　input　image　size,　where　besides　the　original　input　images,　images　with　a　side　length　of　1　/　2　,　1　/　4　,　1　/　8　horizontal　ellipsis　of　that　of　the　original　square　images　are　also　input　into　the　model　by　using　the　resize　command;　(2)　group　convolution　is　used;　and　(3)　global　average　pooling　is　used　because　it　normally　has　less　parameters　compared　with　the　fully　connected　layer.　MobileCrack　will　then　compute　the　combinations　of　different　resized　input　images　and　different　neural　network　structures,　to　find　the　optimal　reduced　image　size　and　neural　network　structure　with　satisfactory　accuracy　using　reasonable　computation　time.　To　verify　the　applicability　of　MobileCrack,　10,000　input　images　with　size　400　x　400　are　trained　for　the　classification　task　of　crack,　sealed　crack,　pavement　marking,　and　pavement　matrix.　Based　on　the　computation　results　of　combinations　of　images　with　different　sizes　(400　x　400　,　200　x　200　,　100　x　100　,　and　50　x　50　)　and　different　stacking　numbers　of　core　modules　n　(3,　4,　5,　and　6),　the　optimal　model　is　determined　as　image　size　200　x　200　and　n　=　4　,　where　the　test　accuracy　is　0.865　within　reasonable　computation　time　(runtime　=　47　ms　to　test　one　image).　This　optimal　model　will　be　automatically　used　for　further　tests　with　image　size　400　x　400　for　a　fast　computation,　which　realizes　the　lightweight　adaptive　goal.　Results　also　show　that　the　test　accuracy　of　MobileCrack　is　higher　than　that　of　the　AlexNet　and　visual　geometry　group　(VGG),　and　the　parameters　of　MobileCrack　are　approximately　1　/　4　of　that　of　the　classic　lightweight　CNN　model　MobileNet,　which　saves　the　storage　space.　It　is　concluded　that　that　the　proposed　adaptive　lightweight　CNN　model,　MobileCrack,　can　be　used　for　the　fast　object　classification　on　asphalt　pavement　crack　images.