Saliency Object Detection Utilizing Adaptive Convolutional Attention and Mask Structure

ZHU Lei; YUAN Jinyao; WANG Wenwu; CAI Xiaoman

doi:10.11999/JEIT240431

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ZHU Lei, YUAN Jinyao, WANG Wenwu, CAI Xiaoman. Saliency Object Detection Utilizing Adaptive Convolutional Attention and Mask Structure[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240431

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ZHU Lei, YUAN Jinyao, WANG Wenwu, CAI Xiaoman. Saliency Object Detection Utilizing Adaptive Convolutional Attention and Mask Structure[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240431

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Saliency Object Detection Utilizing Adaptive Convolutional Attention and Mask Structure

doi: 10.11999/JEIT240431

School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430000, China

Received Date: 2024-05-13
Rev Recd Date: 2024-09-18

Available Online: 2024-09-24

Abstract

Abstract

Objective Salient Object Detection (SOD) aims to replicate the human visual system’s attentional processes by identifying visually prominent objects within a scene. Recent advancements in Convolutional Neural Networks (CNNs) and Transformer-based models have improved performance; however, several limitations remain: (1) Most existing models depend on pixel-wise dense predictions, diverging from the human visual system’s focus on region-level analysis, which can result in inconsistent saliency distribution within semantic regions. (2) The common application of Transformers to capture global dependencies may not be ideal for SOD, as the task prioritizes center-surround contrasts in local areas rather than global long-range correlations. This study proposes an innovative SOD model that integrates CNN-style adaptive attention and mask-aware mechanisms to enhance contextual feature representation and overall performance. Methods The proposed model architecture comprises a feature extraction backbone, contextual enhancement modules, and a mask-aware decoding structure. A CNN backbone, specifically Res2Net, is employed for extracting multi-scale features from input images. These features are processed hierarchically to preserve both spatial detail and semantic richness. Additionally, this framework utilizes a top-down pathway with feature pyramids to enhance multi-scale representations. High-level features are further refined through specialized modules to improve saliency prediction. Central to this architecture is the ConvoluTional attention-based contextual Feature Enhancement (CTFE) module. By using adaptive convolutional attention, this module effectively captures meaningful contextual associations without relying on global dependencies, as seen in Transformer-based methods. The CTFE focuses on modeling center-surround contrasts within relevant regions, avoiding unnecessary computational overhead. Features refined by the CTFE module are integrated with lower-level features through the Feature Fusion Module (FFM). Two fusion strategiesAttention-Fusion and Simple-Fusion—were evaluated to identify the most effective method for merging hierarchical features. The decoding process is managed by the Mask-Aware Transformer (MAT) module, which predicts salient regions by restricting attention to mask-defined areas. This strategy ensures that the decoding process prioritizes regions relevant to saliency, enhancing semantic consistency while reducing noise from irrelevant background information. The MAT module’s ability to generate both masks and object confidence scores makes it particularly suited for complex scenes. Multiple loss functions guide the training process: Mask loss, computed using Dice loss, ensures that predicted masks closely align with ground truth. Ranking loss prioritizes the significance of salient regions, while edge loss sharpens boundaries to clearly distinguish salient objects from their background. These objectives are optimized jointly using the Adam optimizer with a dynamically adjusted learning rate. Results and Discussions Experiments were conducted using the PyTorch framework on an RTX 3090 GPU, with training configurations optimized for SOD datasets. The input resolution was set to 384×384 pixels, and data augmentation techniques, such as horizontal flipping and random cropping, were applied. The learning rate was initialized at 6e–6 and adjusted dynamically, with the Adam optimizer employed to minimize the combined loss functions. Experimental evaluations were performed on four widely used datasets: SOD, DUTS-TE, DUT-OMRON, and ECSSD. The proposed model demonstrated exceptional performance across all datasets, showing significant improvements in Mean Absolute Error (MAE) and maximum F-measure metrics. For instance, on the DUTS-TE dataset, the model achieved an MAE of 0.023 and a maximum F-measure of 0.9508, exceeding competing methods such as MENet and VSCode. Visual comparisons indicate that the proposed method generates saliency maps that closely align with the ground truth, effectively addressing challenging scenarios including fine structures, multiple objects, and complex backgrounds. In contrast, other methods often incorporate irrelevant regions or fail to accurately capture object details. Ablation experiments validated the effectiveness of crucial components. For example, the incorporation of the CTFE module resulted in a reduction of MAE from 0.109 to 0.102. Additionally, the Simple-Fusion strategy outperformed the Attention-Fusion approach, yielding a lower MAE and a higher maximum F-measure score. The integration of IOU and BCE-based edge loss further enhanced boundary sharpness, demonstrating superior performance compared to Canny-based edge loss. Heatmaps illustrate the contributions of the CTFE and MAT modules in emphasizing salient regions while preserving semantic consistency. The CTFE effectively accentuates center-surround contrasts, while the MAT captures global object-level semantics. These visualizations highlight the model’s ability to focus on critical areas while minimizing background noise. Conclusions This study presents a novel SOD framework that integrates CNN-style adaptive attention with mask-aware decoding mechanisms. The proposed model addresses the limitations of existing approaches by enhancing semantic consistency and contextual representation while avoiding excessive dependence on global variables. Comprehensive evaluations demonstrate its robustness, generalization capability, and significant performance enhancements across multiple benchmarks. Future research will investigate further optimization of the architecture and its application to multimodal SOD tasks, including RGB-D and RGB-T saliency detection.
- Saliency Object Detection (SOD),
- Convolutional Neural Network (CNN)-style adaptive attention,
- Mask attention,
- Feature fusion

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References(28)

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