The　safety　of　autonomous　driving　is　closely　linked　to　accurate　depth　perception.　With　the　continuous　development　of　autonomous　driving,　depth　completion　has　become　one　of　the　crucial　methods　in　this　field.　However,　current　depth　completion　methods　have　major　shortcomings　in　small　objects.　To　solve　this　problem,　this　paper　proposes　an　end-to-end　architecture　with　adaptive　spatial　feature　fusion　by　encoder-decoder　(ASFF-ED)　module　for　depth　completion.　The　architecture　is　built　on　the　basis　of　the　network　architecture　proposed　in　this　paper,　and　is　able　to　extract　depth　information　adaptively　with　different　weights　on　the　specified　feature　map,　which　effectively　solves　the　problem　of　insufficient　depth　accuracy　of　small　objects.　At　the　same　time,　this　paper　also　proposes　a　depth　map　visualization　method　with　a　semi-quantitative　visualization,　which　makes　the　depth　information　more　intuitive　to　display.　Compared　with　the　currently　available　depth　map　visualization　methods,　this　method　has　stronger　quantitative　analysis　and　horizontal　comparison　ability.　Through　experiments　of　ablation　study　and　comparison,　the　results　show　that　the　method　proposed　in　this　paper　exhibits　a　lower　root-mean-squared　error　(RMSE)　and　better　small　object　detection　performance　on　the　KITTI　dataset.