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.

As the most crucial medium for multimedia presentation, liquid crystal displays (LCDs) is integral to numerous industries, making precise defect detection essential to ensure display quality and user experience. However, high accuracy defect detection of LCDs remains a significant challenge due to large-scale variations and high inter-class similarity, especially under the setting of few-shot learning. To address these challenges, we propose a few-shot defect detection network, namely HiSCAD-Net. Specifically, we design an auxiliary branch for hierarchical scale enhancement, which introduces additional objectness and classification constraints based on object pyramid sampling. Moreover, to tackle the misclassification caused by inter-class similarity, we introduce an object-level contrastive encoding to encourage class-discriminative feature learning, which enforces zero distance between objects of the same class, while ensuring that the distance between objects of different classes remains above a predefined threshold. Finally, we propose an adaptive decoupling module to mitigate interference between classification and regression tasks given limited training samples, thereby improving both tasks during decoding. To support benchmarking in few-shot LCD defect detection, we propose a new dataset named FSLCD. Experimental results on the FSLCD, NEU-DET, and PKU-Market-Phone datasets demonstrate that the proposed model outperforms 18 state-of-the-art methods, validating its effectiveness and generalizability. Notably, in the 10-shot setting, our model achieved a mean Average Precision (mAP) of 62.0%, surpassing the state-of-the-art by 10.4%.

As a next-generation automotive display technology, Augmented Reality Head-Up Display (AR-HUD) has demonstrated immense potential in reshaping driving safety and enhancing the human–computer interaction experience. To address the challenges of barrel distortion and perspective distortion inherent in HUD systems, we propose a joint-learning-based dual-path pre-correction method. This approach employs a shared encoder to extract image features, which are then decoupled into two parallel branches: a classification branch and a distortion flow prediction branch. Building upon this architecture, a model-fitting method is introduced to estimate the distortion model parameters in the parameter space using the predicted distortion types and flows, thereby reconstructing a refined distortion flow. Finally, image rectification is achieved through a resampling method. On the ARHDD dataset, the proposed method achieves a PSNR of 24.617 dB (barrel) and 25.062 dB (perspective), an SSIM of 0.845 and 0.873, and an NRMSE of 0.163 and 0.157, respectively. On the Places 365 dataset, it achieves a PSNR of 23.914 dB (barrel) and 21.870 dB (perspective), an SSIM of 0.812 and 0.748, and an NRMSE of 0.174 and 0.211, respectively. Both quantitative and qualitative comparative experiments against other state-of-the-art methods demonstrate that the proposed approach achieves superior correction performance for both types of distortion. Finally, the simulation verification of the HUD system proved that this correction method demonstrated excellent potential, but further verification is still needed in a real or semi-real environment.