Conventional practices of bridge visual inspection present several limitations, including a tedious process analyzing images manually to identify potential damages. Vision-based techniques, particularly Deep Convolutional Neural Networks, have been widely investigated automatically identify, localize, and quantify defects in images. However, massive datasets with different annotation levels are...