In　recent　years,　computer　vision　applied　in　industrial　application　scenarios　has　been　attracting　attention.　A　lot　of　work　has　been　made　to　present　approaches　based　on　visual　perception,　ensuring　the　safety　during　the　processes　of　industrial　production.　However,　much　less　effort　has　been　done　to　assess　the　perceptual　quality　of　corrupted　industrial　images.　In　this　paper,　we　construct　an　Industrial　Scene　Image　Database　(ISID),　which　contains　3000　distorted　images　generated　through　applying　different　levels　of　distortion　types　to　each　of　the　50　source　images.　Then,　the　subjective　experiment　is　carried　out　to　gather　the　subjective　scores　in　a　well-controlled　laboratory　environment.　Finally,　we　perform　comparison　experiments　on　ISID　database　to　investigate　the　performance　of　some　objective　image　quality　assessment　algorithms.　The　experimental　results　show　that　the　state-of-the-art　image　quality　assessment　methods　have　difficulty　in　predicting　the　quality　of　images　that　contain　multiple　distortion　types.　©　2022,　Springer　Nature　Singapore　Pte　Ltd.