Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, and complex imaging conditions, mainly for two reasons: (1) accurate location of salient objects; and (2) subtle boundaries of salient objects. This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI. Specifically, the proposed semantic guided decoder (SGD) roughly but accurately locates the multi-scale object by aggregating multiple high-level features, and then this global semantic information guides the integration of subsequent features in a step-by-step feedback manner to make full use of deep multi-level features. Simultaneously, the proposed parallel attention fusion (PAF) module combines cross-level features and semantic-guided information to refine the object’s boundary and highlight the entire object area gradually. Finally, the proposed network architecture is trained through an end-to-end fully supervised model. Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that our SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.

Drone-based object detection has been widely applied in ground object surveillance, urban patrol, and some other fields. However, the dramatic scale changes and complex backgrounds of drone images usually result in weak feature representation of small objects, which makes it challenging to achieve high-precision object detection. Aiming to improve small objects detection, this paper proposes a novel cross-scale knowledge distillation (CSKD) method, which enhances the features of small objects in a manner similar to image enlargement, so it is termed as ZoomInNet. First, based on an efficient feature pyramid network structure, the teacher and student network are trained with images in different scales to introduce the cross-scale feature. Then, the proposed layer adaption (LA) and feature level alignment (FA) mechanisms are applied to align the feature size of the two models. After that, the adaptive key distillation point (AKDP) algorithm is used to get the crucial positions in feature maps that need knowledge distillation. Finally, the position-aware L2 loss is used to measure the difference between feature maps from cross-scale models, realizing the cross-scale information compression in a single model. Experiments on the challenging Visdrone2018 dataset show that the proposed method draws on the advantages of the image pyramid methods, while avoids the large calculation of them and significantly improves the detection accuracy of small objects. Simultaneously, the comparison with mainstream methods proves that our method has the best performance in small object detection.

Despite the significant advancements made by deep visual networks in detecting surface defects at a regional level, the challenge of achieving high-quality pixel-wise defect detection persists due to the varied appearances of defects and the limited availability of data. To address the over-reliance on defect appearance and enhance the accuracy of defect segmentation, we proposed a Transformer-based Siamese network with change awareness, which formulates the defect segmentation under a complex background as change detection to mimic the human inspection process. Specifically, we introduced a novel multi-class balanced contrastive loss to guide the Transformer-based encoder, enabling it to encode diverse categories of defects as a unified, class-agnostic difference between defective and defect-free images. This difference is represented through a distance map, which is then skip-connected to the change-aware decoder, assisting in localizing pixel-wise defects. Additionally, we developed a synthetic dataset featuring multi-class liquid crystal display (LCD) defects set within a complex and disjointed background context. In evaluations using our proposed and two public datasets, our model outperforms leading semantic segmentation methods while maintaining a relatively compact model size. Furthermore, our model achieves a new state-of-the-art performance compared to semi-supervised approaches across various supervision settings. Our code and dataset are available at https://github.com/HATFormer/CADNet.