As　a　key　factor　in　the　milling　process,　the　wear　status　of　the　milling　cutter　has　a　significant　impact　on　the　machining　quality　of　the　workpiece.　To　detect　wear　on　a　milling　machine　efficiently　and　precisely,　this　paper　presents　the　development　of　a　milling　machine　wear　detection　system　based　on　machine　vision　and　digital　image　processing.　The　system　including　link　mechanisms　and　industrial　camera　is　designed　for　auxiliary　localization　and　collection　of　on-machine　images　of　milling　cutter　status.　The　image　preprocessing　method　based　on　automatic　threshold　segmentation　and　Canny　edge　detection　operator　is　proposed　to　identify　the　edge　of　cutter　wear.　The　Maximum　connected　domains　algorithm　is　used　to　screen　the　wear　area　of　the　milling　cutter　and　the　amount　of　wear　is　obtained　based　on　a　calibrated　scaling　method.　Experimental　results　show　that　the　proposed　system　is　suitable　for　industrial　use　due　to　its　rapid　detection　speed　and　strong　recognition　accuracy,　which　are　desirable　for　engineering　applications.　©2024　IEEE.