Defect Detection On Manufacturing Product Images by Applying Weekly Detector Algorithm using Deep Learning Technology - Dr. S.Praveen Kumar , Dr.K.Naveen Kumar, Dr.Y.Srinivas, Dr.G.V.S Rajkumar (International Journal of Advanced Science and Technology, 2020)
Deep learning permits process models that square measure created out of diverse handling layers to findout illustration of data with various levels of abstraction. The paper is especially aimed towards makingAssociate intending innovative pc vision program that will act as Associate in aid to work out thedefective surfaces on any product. To realize such machine-controlled approaches, it's needed to utilizeone in every of the many Deep Learning Algorithms that exist now a days. VGG net was chosen forassessing the desired results.Additionally, the scale of information set was augmented to confirm that the trained model doesn't overmatch the data. As a result, four totally different classifications were obtained once the complete datasetis fed into the trained model.For segmentation we have a tendency to enforced feeble detector to induceareas with defects to be thermally known and localize their class-specific areas from the categoryactivation maps. This paper demonstrates that our network is in a position to classify and localize thediscriminative areas on the defects of the photographs fed.