自动检测钢缺陷的DeepLabV3 +性能分析

我们的工作在大量钢图像上对DeepLabV3 +的不同主干进行了实验，旨在自动检测不同类型的钢缺陷。我们的方法应用随机加权增强来平衡训练集中的不同缺陷类型。..

Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection

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.