In this paper, we discuss and try a multi-image fusion approach with a multibranch Convolutional Neural Network (CNN) that implies a MultiHeadAttention (MHA) technique in the fusion center. This work studies the employment of the architecture on actual data containing different images of the same USB device. The images differ in the direction of the light at the moment of the acquisition. We observed that instead of employing a simple concatenation fusion of the outputs, the network architecture could employ a multibranch classification featurewise, which utilizes a multi-head attention mechanism instead of a channel attention one.
Defect detection MultiHeadAttention Fusion model on images acquired with different light sources
Somero M.;Urli F.;Snidaro L.;
2024-01-01
Abstract
In this paper, we discuss and try a multi-image fusion approach with a multibranch Convolutional Neural Network (CNN) that implies a MultiHeadAttention (MHA) technique in the fusion center. This work studies the employment of the architecture on actual data containing different images of the same USB device. The images differ in the direction of the light at the moment of the acquisition. We observed that instead of employing a simple concatenation fusion of the outputs, the network architecture could employ a multibranch classification featurewise, which utilizes a multi-head attention mechanism instead of a channel attention one.File in questo prodotto:
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