  Our works experimented DeepLabV3+ with different backbones on a large volume
of steel images aiming to automatically detect different types of steel
defects. Our methods applied random weighted augmentation to balance different
defects types in the training set. And then applied DeeplabV3+ model three
different backbones, ResNet, DenseNet and EfficientNet, on segmenting defection
regions on the steel images. Based on experiments, we found that applying
ResNet101 or EfficientNet as backbones could reach the best IoU scores on the
test set, which is around 0.57, comparing with 0.325 for using DenseNet. Also,
DeepLabV3+ model with ResNet101 as backbone has the fewest training time.
