Recently,　there　is　a　significant　success　in　the　current　object　detection　task.　However,　existing　research　has　demonstrated　that　detectors　trained　on　regular　images　experience　a　significant　decrease　when　facing　to　wild　scences　such　as　haze　scence.　Although　enhancing　detection　performance　is　possible　through　multi-stage　adaptive　parameter-guided　image　enhancement,　this　method　fails　to　consistently　enhance　downstream　object　detection　performance.　To　address　this　issue,　we　propose　an　end-To-end　object　detection　framework　that　utilizes　weakly　supervised　parameter-guided　feature　fusion　to　improve　detection　performance.　Specifically,　we　introduce　the　Self-Adaptive　Feature　Fusion　Module　(SAFFM)　that　dynamically　guides　feature　fusion　in　the　detection　network　through　the　haze　image　information　encoded　by　the　Prior　Knowledge　(PK)　components.　By　the　joint　learning　of　the　PK　components　and　the　detection　network　in　an　end-To-end　manner,　we　ensure　that　the　PK　components　weakly　supervise　the　feature　fusion　process　to　enhance　detection　performance.　Additionally,　we　propose　a　Downsampling　Feature　Enhancement　Module　(DFEM)　that　utilizes　multibranch　down-sampling　operators　and　reparameterization　to　enhance　the　feature　extraction　capability　of　the　network,　allowing　it　to　capture　more　latent　information.　Comprehensive　experiments　on　synthetic　datasets　and　real　datasets,　demonstrated　that　the　detection　performance　of　the　proposed　network　significantly　outperforms　other　state-of-The-Art　detection　methods.　©　2023　IEEE.