implementation of the dcl algorithm using pre-trained vgg16 model for salient object detection in complex environments

使用深度神经网络提高显著目标检测的精度

作者聚焦：智能电网应用的显著对象检测增强功能

用于复杂环境中显著目标检测的端到端深度神经网络

Our research range from salient object detection, computer vision, image processing and more. We want to respond to the application of salient object detection such as its use in smart grid for inspecting insulators. The next research advancement involves transferring salient object algorithms for learning in the context of smart grids.The convolutional neural networks can more effectively act as salient information from images, enhancing the processing of salient object detection. Therefore, they advance the research in salient object detection. Our research outcome improves the accuracy of salient object detection, and allow for the transfer of algorithms such as their application in smart grids for effectively segmenting and detecting insulators.

使用预训练的 VGG16 模型实现 DCL 算法在复杂环境中的显著目标检测

首先，在 Python 中加载深度学习库，例如 PyTorch。将 Torch 和 Torchvision 模型导入为模型。接下来，加载预训练的 VCG16 模型。要生成 DCL 算法的伪代码，请在输入字段中提供图像数据集 SOD，并使用训练 DCL 模型作为输出字段。现在使用 VGG16 骨干网络初始化 DCL 模型。对图像数据集进行预处理，然后将数据集拆分为训练集和验证集。定义用于训练 DCL 模型的损失函数。将训练超参数设置为 0.0001 表示学习率，将 50 设置为训练周期数，将 8 设置为批处理大小，将 Adam 设置为优化器。结合 DCL 和 DEDN 网络的输出，并使用全连接条件随机场模型细化显著性图。要处理映像，请单击运行代码以调出 GUI 界面。现在按“打开图像”选择要检测的选定图像。然后按“显示图像”以显示所选图像。单击“开始检测”以检测所选图像。最后，按 选择安全路径 并选择适当的文件位置以保存图像结果。从算法中删除 DCL 模型会导致 F beta 值降低和 E MAE 值增加。此算法仅删除 DEDN 结构。与整个模块相比，观察到 F beta 值的类似下降和 E MAE 值的增加。DCL算法在SOD数据库中检测图像时描述了目标边界，但难以有效过滤背景。然而，DEDN算法加强了目标边界，但抑制了背景冗余信息。

implementation-dcl-algorithm-using-pre-trained-vgg16-model-for

Research

JoVE Journal

Engineering

Implementation of the DCL Algorithm Using Pre-Trained VGG16 Model for Salient Object Detection in Complex Environments

111 次观看. 首先，在 Python 中加载深度学习库，例如 PyTorch。将 Torch 和 Torchvision 模型导入为模型。接下来，加载预训练的 VCG16 模型。要生成 DCL 算法的伪代码，请在输入字段中提供图像数据集 SOD，并使用训练 DCL 模型作为输出字段。现在使用 VGG16 骨干网络初始化 DCL 模型。对图像数据集进行预处理，然后将数据集拆分为训练集和验证集。定义用于训练 DCL 模型的损失函数。将?...

视频: 使用预训练的 VGG16 模型实现 DCL 算法在复杂环境中的显著目标检测

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted environments. In light of this pressing concern, this article presents an end-to-end deep neural network that aims to detect salient objects within complex environments. The study introduces an end-to-end deep neural network that aims to detect salient objects within complex environments. Comprising two interrelated components, namely a pixel-level multiscale full convolutional network and a deep encoder-decoder network, the proposed network integrates contextual semantics to produce visual contrast across multiscale feature maps while employing deep and shallow image features to improve the accuracy of object boundary identification. The integration of a fully connected conditional random field (CRF) model further enhances the spatial coherence and contour delineation of salient maps. The proposed algorithm is extensively evaluated against 10 contemporary algorithms on the SOD and ECSSD databases. The evaluation results demonstrate that the proposed algorithm outperforms other approaches in terms of precision and accuracy, thereby establishing its efficacy in salient object detection within complex environments.

The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit ...

科研

工程学

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.