.png){kind=icon}
.png){kind=icon}
Return
CGDINet: A Deep Learning-Based Salient Object Detection Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3525303.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Salient object detection (SOD) is a key preprocessing step in computer vision, widely used in object tracking, action recognition, and image retrieval, among other fields. However, traditional SOD algorithms often face issues such as rough object boundaries, incomplete extraction of global image features, and insufficient attention to key areas. To address these problems, an improved significance object detection network-CGDINet (Coordinate Attention-Group Consensus Aggregation Module-Depth Auxiliary Module-Inverse Saliency Pyramid Reconstruction Network)-is proposed. CGDINet introduces the Group Consensus Aggregation Module (GCAM) embedded with the Coordinate Attention (CA) mechanism, named CAGM (Coordinate Attention-Group Consensus Aggregation Module), to enhance global feature capture capabilities and improve the processing of directional features. Additionally, the Depth Auxiliary Module (DAM) is incorporated to enhance the focus on important regions. Experiments were conducted on five public datasets (DUT-OMRON, ECSSD, PASCAL-S, DUTS-TE, and HKU-IS). The results show that CGDINet outperforms other mainstream significance object detection models in evaluation metrics such as maxF(beta) , S-alpha , and MAE, with almost no increase in computational cost (FLOPs) and parameters. The experimental results validate that CGDINet can effectively address the issues of incomplete global feature extraction and insufficient attention to key areas, thereby significantly enhancing the performance of significance object detection
Keywords:
Feature extraction
Transformers
Context modeling
Object detection
Computational modeling
Accuracy
Image edge detection
Dams
Convolutional neural networks
Attention mechanisms
Salient object detection
deep learning
InSPyReNet
CAGM
DAM