Uneven illumination surface defects inspection based on convolutional neural network

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination

Diphoton excess at 750 GeV: gluon-gluon fusion or quark-antiquark annihilation?

Recently, ATLAS and CMS collaboration reported an excess in the diphoton events, which can be explained by a new resonance with mass around 750 GeV. In this work, we explored the possibility of identifying if the hypothetical new resonance is produced through gluon-gluon fusion or quark-antiquark annihilation, or tagging the beam. Three different observables for beam tagging, namely the rapidity and transverse momentum distribution of the diphoton, and one tagged bottom-jet cross section, are proposed. Combining the information gained from these observables, a clear distinction of the production mechanism for the diphoton resonance is promising.Comment: 20 pages, 7 figure