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.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
Concatenated Skip Connection •
Convolution •
Dense Block •
Dense Connections •
DenseNet •
Depthwise Convolution •
Depthwise Separable Convolution •
Dropout •
EfficientNet •
Global Average Pooling •
Inverted Residual Block •
Kaiming Initialization •
Max Pooling •
Pointwise Convolution •
ReLU •
Residual Block •
Residual Connection •
ResNet •
RMSProp •
Sigmoid Activation •
Softmax •
Squeeze-and-Excitation Block •
Swish
