Due　to　the　edge　jagged　and　blurred　problem　in　conventional　deep　learning-based　optical　flow　estimation　methods,　an　edge　detection-based　optical　flow　model　(EDOF)　is　proposed　in　this　paper　to　improve　the　accuracy　of　optical　flow　estimation.　In　this　model,　the　feature　extraction　module　EDNet　is　used　to　obtain　the　features　with　the　edge　information　of　the　objects　in　images,　while　the　other　feature　extraction　module　OFNet　extracts　the　convolutional　features　with　other　common　features　such　as　the　texture　and　color　information　of　the　object　and　others.　The　two　kinds　of　features　are　fused　to　EstiNet,　and　then　the　estimation　result　is　obtained　by　input　the　fused　features　into　the　optical　flow　network.　Experiments　on　the　public　MPI　Sintel　and　Flying　Chairs　datasets　show　that　the　EDOF　method　can　reduce　the　average　endpoint　error　of　optical　flow　estimation.　©　2021　IEEE.