Atmospheric　haze　significantly　impairs　the　performance　of　computer　vision　tasks　such　as　image　dehazing　and　object　detection.　Existing　methods　often　address　these　tasks　independently,　failing　to　provide　an　integrated　solution　that　can　effectively　handle　hazy　conditions　while　maintaining　accurate　object　detection.　This　paper　presents　Mix-YOLONet,　a　novel　joint　network　architecture　designed　to　tackle　both　image　dehazing　and　object　detection　simultaneously.　Mix-YOLONet　leverages　the　powerful　image　restoration　capabilities　of　U-Net-like　architecture　and　integrates　it　with　the　detection　precision　of　a　YOLO　head.　Additionally,　we　integrated　Mix　Structure　Blocks　(MSB)　to　our　joint　network　and　experimented　various　configurations　at　strategic　locations　to　enhance　feature　extraction　and　context　aggregation.　Through　extensive　experiments,　we　demonstrate　that　Mix-YOLONet　achieves　superior　performance　in　both　dehazing　and　object　detection　tasks　under　challenging　visibility　conditions,　outperforming　state-of-the-art　methods　on　three　benchmark　datasets　both　quantitatively　and　qualitatively.　The　proposed　joint　network　not　only　improves　the　clarity　of　hazy　images　but　also　ensures　accurate　object　localization,　paving　the　way　for　more　robust　object　detection　in　adverse　environmental　conditions.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.