Haze　seriously　affects　the　reliability　of　industrial　systems,　especially　vision-based　outdoor　industrial　systems　such　as　autopilot　systems.　A　majority　of　existing　dehazing　methods　are　not　specifically　designed　for　industrial　systems　and　do　not　consider　the　reliability　and　resource　cost　of　industrial　system　implementation.　In　this　article,　a　novel　meta-attention　dehazing　network　(MADN)　is　proposed　for　direct　restoration　of　clear　images　from　hazy　images　without　using　the　physical　scattering　model.　Combined　with　parallel　operation　and　enhancement　modules,　the　meta-network　automatically　selects　the　most　suitable　dehazing　network　structure　based　on　the　current　input　hazy　image　by　a　meta-attention　module.　In　addition,　a　novel　feature　loss　calculated　by　the　meta-network　is　proposed,　which　can　accelerate　the　convergence　of　the　dehazing　network　to　meet　the　application　requirements　of　practical　industrial　systems.　A　large　number　of　experimental　results　on　synthetic　and　real-world　datasets　show　that　the　proposed　MADN　satisfies　the　needs　of　industrial　systems.