高精度图像识别关键技术应用进展与挑战

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中图分类号：TP391 文献标识码：A文章编号：1006-8228（2026）02-07-04

Abstract：Imagerecognitiontechologyplaysanextremelyimportantroleinfieldssuchasautonomousdriving，medicaldiagnosis， andintellgentretailCurrentlyig-precisionimagerecognitiontenologyfceschalengessuchasighcomputationalcosts， insuficientcross-domaingeneraationcapabites，ndpormodelinterpretabilityToadressthseisues，thispaperfocuseon improvementsinmodelparametereficiencythroughtheTransformerarchitecture；synergisticenhancementsindetectionaccuracy andeficiencyviaprogrammablegradientinformationBiFPfusionandAnchor-Freemechanisms；andtheapplicationofultiattentionstrategiesinfinegradfeatureseleionandrconionAddressingurrentbotlenecksweproposefutureiectos： acceleratingthedeploymentofVision-LanguageModels（VLMs）viaknowledgedistlltionandmixed-precisionquantization; enhancingreal-tiedetectionmodelsrobustnessthroughfeaturedecouplingandincrementallearning;designingaunifiedperception to-decisionframeworkforobotics；andstrengtheningthesynergybetweeninterpretabilityandfine-grainedrecognitionTework servesasa reference for both research and engineering applications.

Keywords：Image Recognition;Deep Learning;Multi-Method Fusion; Interpretability

0引言

图像识别技术是计算机视觉领域的核心任务之一，通过分析图像内容，计算机能够自动检测识别图像中的物体、场景和文本内容，并将其转化为可理解和可操作的信息。（剩余6636字）