PCB Defect Detection Using Deep Learning Methods
Xing Wu, Yuxi Ge, Qingfeng Zhang, Dali Zhang
Abstract
As a component widely used in electronic products, Printed Circuit Board(PCB) plays an extremely important role in our life. Due to technical limitations, PCB with defects will inevitably appear in the production process. In order to ensure high yield and save labor cost, this paper applied two kinds of target detection network to PCB defect detection and classification tasks. Experiments show that the two methods used in the two different distribution of data sets achieved high accuracy.
Topics & Concepts
Printed circuit boardComputer scienceProcess (computing)Deep learningElectronic componentProduction (economics)Yield (engineering)Component (thermodynamics)Reliability engineeringArtificial intelligenceEngineeringElectrical engineeringMaterials scienceOperating systemMetallurgyPhysicsEconomicsThermodynamicsMacroeconomicsIndustrial Vision Systems and Defect DetectionImage and Object Detection TechniquesIntegrated Circuits and Semiconductor Failure Analysis