In recent years, numerous advanced lightweight models have been proposed for salient object detection (SOD) in optical remote sensing images (ORSI). However, most methods still face challenges such as performance limitations and imbalances between accuracy and computational cost. To address these issues, we propose SggNet, a novel semantic- and graph-guided lightweight network for ORSI-SOD. The SggNet adopts a classical encoder-decoder structure with MobileNet-V2 as the backbone, ensuring optimal parameter utilization. Furthermore, we design an Efficient Global Perception Module (EGPM) to capture global feature relationships and semantic cues through limited computational costs, enhancing the model’s ability to perceive salient objects in complex scenarios, and a Semantic-Guided Edge Awareness Module (SEAM) that leverages the semantic consistency of deep features to suppress background noise in shallow features, accurately predict object boundaries, and preserve the detailed shapes of salient objects. To further efficiently aggregate multi-level features and preserve the integrity and complexity of overall object shape, we introduce a Graph-Based Region Awareness Module (GRAM). This module incorporates non-local operations under graph convolution domain to deeply explore high-order relationships between adjacent layers, while utilizing depth-wise separable convolution blocks to significantly reduce computational cost. Extensive quantitative and qualitative experiments demonstrate that the proposed model achieves excellent performance with only 2.70 M parameters and 1.38 G FLOPs, while delivering an impressive inference speed of 108 FPS, striking a balance between efficiency and accuracy to meet practical application needs.

Considering the large-area distribution, smooth brightness gradients, and blurred boundaries of Mura defects in real industrial scenarios, as well as the challenge of balancing accuracy and efficiency in existing methods, we propose a lightweight deep learning-based detection method for large-area Mura defects, termed SIFNet. The SIFNet adopts a classical encoder-decoder architecture with MobileNet-V2 as the backbone. Furthermore, we design a Graph-based Semantic Interscale-fusion Block (GSIB) that integrates the Semantic Fluid Aggregation Module (SFAM) and the Semantic Graph Inference Module (SGIM) to collaboratively extract high-level semantic features across multiple scales and establish abstract semantic representations for accurately localizing large-area Mura defects. Specifically, SFAM leverages a global attention mechanism to extract cross-spatial semantic flows, guiding the model to focus on potential brightness anomaly regions in the image and SGIM explicitly models the semantic relationships between multi-scale features using graph convolution, enhancing the model's ability to interpret regions with blurred boundaries and ambiguous structures. To further improve the model’s sensitivity to edges in regions with smooth brightness transitions, we introduce a NeighborFusion Edge Enhancement Module (NEEM). This module integrates depthwise separable convolutions with a spatial attention mechanism and introduces a CrossNorm-based feature alignment strategy to enhance spatial collaboration across feature layers. Additionally, an edge enhancement mechanism is employed to significantly improve the model’s ability to delineate blurred Mura defect boundaries, while keeping computational cost low and strengthening edge perception and representation. Extensive quantitative and qualitative experiments on three large-area Mura defect datasets constructed in this study demonstrate that SIFNet achieves excellent detection performance with only 3.92M parameters and 6.89 GFLOPs, striking an effective balance between accuracy and efficiency, and fully meeting the demands of industrial deployment.