The　article　proposes　a　qualitative　identification　scheme　of　fluorescent　immunoassay　strips　based　on　residual　networks　to　address　problems　such　as　poor　strip　positioning　accuracy　and　inadequate　strip　size　specifications　in　current　photoelectric　fluorescent　immunoassay　quantitative　detection　systems,　which　result　in　low　accuracy　and　detection　efficiency.　The　proposed　method　employs　the　Hough　line　detection　algorithm,　which　is　based　on　Canny　edge　detection,　to　extract　the　tilt　angle　of　the　test　strip.　It　then　combines　this　with　contour　extraction　of　the　strip　image　to　calculate　its　contour　center.　By　utilizing　the　test　strip　tilt　angle　and　contour　center,　the　article　accurately　locates　the　test　strip　position.　The　residual　network　is　utilized　for　extracting　strip　features,　while　the　extreme　learning　machine　is　employed　for　discriminating　the　validity　and　positive/negative　results　of　the　fluorescent　strips.　Validation　experiments　on　a　novel　coronavirus　fluorescent　immunoassay　strip　demonstrate　that　the　residual　network　model　based　on　extreme　learning　machine　proposed　in　this　article　achieves　100%　accuracy,　precision,　recall,　and　F1-score　values　for　strip　classification,　effectively　improving　the　recognition　accuracy　and　detection　efficiency　of　the　fluorescent　immunoassay　quantitative　detection　system.　　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.