论文标题
关于自动钢缺陷检测的DeepLabv3+性能的分析
Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection
论文作者
论文摘要
我们的作品在大量的钢图像上进行了DeepLabv3+的试验，旨在自动检测不同类型的钢缺陷。我们的方法应用了随机加权增强，以平衡训练集中的不同缺陷类型。然后将DeepLabv3+模型应用于三种不同的骨干，Resnet，Densenet和EfficityNet，以分割钢图像的分段叛逃区域。根据实验，我们发现将Resnet101或EfficityNet应用于骨架可以达到测试集上最佳的IOU分数，该分数约为0.57，与使用Densenet的0.325相比。同样，具有RESNET101作为骨干的DeepLabv3+模型的训练时间最少。
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.
