Compared　with　natural　images,　underwater　images　are　usually　degraded　with　blur,　scale　variation,　colour　shift　and　texture　distortion,　which　bring　much　challenge　for　computer　vision　tasks　like　object　detection.　In　this　case,　generic　object　detection　methods　usually　fail　to　achieve　satisfactory　performance.　The　main　reason　is　considered　that　the　current　methods　lack　sufficient　discriminativeness　of　feature　representation　for　the　degraded　underwater　images.　A　a　novel　multi-scale　feature　representation　and　interaction　network　for　underwater　object　detection　is　proposed,　in　which　two　core　modules　are　elaborately　designed　to　enhance　the　discriminativeness　of　feature　representation　for　underwater　images.　The　first　is　the　Context　Integration　Module,　which　extracts　rich　context　information　from　high-level　features　and　is　integrated　with　the　feature　pyramid　network　to　enhance　the　feature　representation　in　a　multi-scale　way.　The　second　is　the　Dual-refined　Attention　Interaction　Module,　which　further　enhances　the　feature　representation　by　sufficient　interactions　between　different　levels　of　features　both　in　channel　and　spatial　domains　based　on　attention　mechanism.　The　proposed　model　is　evaluated　on　four　public　underwater　datasets.　The　experimental　results　compared　with　state-of-the-art　object　detection　methods　show　that　the　proposed　model　has　leading　performance,　which　verifies　that　it　is　effective　for　underwater　object　detection.　In　addition,　object　detection　experiments　on　a　foggy　dataset　of　Real-world　Task-driven　Testing　Set　(RTTS)　and　the　natural　image　dataset　of　pattern　analysis　statistical　modelling　and　computational　learning,　visual　object　classes　(PASCAL　VOC)　are　conducted.　The　results　show　that　the　proposed　model　can　be　applied　on　the　degraded　dataset　of　RTTS　but　fails　on　PASCAL　VOC.