However, applying Grad-CAM to embedding networks raises significant challenges because embedding networks are trained by millions of dynamically paired examples (e.g. triplets). To overcome these challenges, we propose an adaptation of the Grad-CAM method for embedding networks. First, we aggregate ...
While Grad-CAM effectively identifies key regions for crack detection, it may overlook less prominent crack pixels. Given the elongated nature of cracks, the up-sampling process used to generate the crack activation map can inadvertently activate surrounding pixels, potentially reducing the precision of...
While Grad-CAM effectively identifies key regions for crack detection, it may overlook less prominent crack pixels. Given the elongated nature of cracks, the up-sampling process used to generate the crack activation map can inadvertently activate surrounding pixels, potentially reducing the precision of...
While Grad-CAM effectively identifies key regions for crack detection, it may overlook less prominent crack pixels. Given the elongated nature of cracks, the up-sampling process used to generate the crack activation map can inadvertently activate surrounding pixels, potentially reducing the precision of...