To begin, load a deep learning library in Python, such as PyTorch. Import Torch and Torchvision Models as models. Next, load the pre-trained VCG16 model.
To generate the pseudo code of the DCL algorithm, provide image dataset SOD into the input field and used train DCL model as the output field. Now initialize the DCL model with the VGG16 backbone network. Pre-process the image dataset, then split the dataset into training and validation sets.
Define the loss function for training the DCL model. Set the training hyperparameters as 0.0001 for learning rate, 50 as the number of training epochs set, eight as the batch size, and Adam as the optimizer. Combine the outputs of the DCL and DEDN networks and refine the saliency map using a fully connected conditional random field model.
To process the image, click on the run code to bring up the GUI interface. Now press Open Image to choose the selected image for detection. Then press Display Image to show the selected image.
Click on Start Detection to detect the selected image. Lastly, press Select the Safe Path and choose the appropriate file location to save the image results. The removal of the DCL model from the algorithm caused a decrease in F beta value and an increase in the E MAE value.
This algorithm only removes the DEDN structure. A similar decrease in the F beta value and an increase in the E MAE value were observed compared to the complete module. The DCL algorithm described the target boundary when detecting images in the SOD database, but struggled to effectively filter the background.
However, the DEDN algorithm strengthened the target boundary, but suppressed background redundancy information.
