A safety-critical control scheme based on fully actuated system approach (FASA) framework is developed for spacecraft relative motion tracking under external disturbances and multiple forbidden regions. For tracking performance, the nominal controller is designed by using the FASA framework, such that the controller design process can be simplified. For safety constraints, a disturbance-tolerant control barrier function incorporating low-pass filtered disturbance compensation is introduced to mitigate interference effects. Furthermore, a sequential correction strategy is developed to resolve safety constraints through offline-computed quadratic program (QP) solutions, which can eliminate dep... More >

Bridge surface defect detection plays a critical role in ensuring traffic safety and facilitating infrastructure maintenance. A lightweight object detection network based on YOLOv10, termed LBSD-YOLO, is developed to achieve high detection accuracy while maintaining high efficiency for deployment on resource-constrained devices. The proposed framework consists of three main components: a feature extraction backbone, a feature fusion neck, and a detection head. In the backbone, the C2f\_FEMA (C2f with Feature Enhancement and Multi-branch Attention) module and the LAEDS (Lightweight Adaptive Encoder–Decoder for Sampling) spatial attention module are incorporated to enhance multi-scale featur... More >

Salient object detection in RGB-D imagery remains challenging due to inconsistent depth quality and suboptimal cross-modal fusion strategies. This paper presents a novel dual-stream architecture that integrates contextual feature refinement with adaptive attention mechanisms for robust RGB-D saliency detection. We extract two features from the ResNet-50 backbone for both the RGB and depth streams, capturing low-level spatial details and high-level semantic representations. We introduce a Contextual Feature Refinement Module (CFRM) that captures multi-scale dependencies through parallel dilated convolutions, enabling hierarchical context aggregation without substantial computational overhead.... More >

Fire incidents cause devastating environmental damage and human casualties, necessitating robust automated detection systems. Existing fire recognition methods struggle with visual ambiguities, illumination variations, and computational constraints, while current attention mechanisms lack hierarchical integration for comprehensive feature refinement. We propose a cascaded multi-attention architecture that combines Multi-Scale Strip Attention (MSSA), Optimized Spatial Attention (OSA), and the Convolutional Block Attention Module (CBAM) to enhance fire detection. MSSA employs three-scale orthogonal strip pooling to capture fire patterns across varying spatial extents through horizontal and ver... More >

RGB–thermal (RGB-T) salient object detection exploits complementary cues from visible and thermal sensors to maintain reliable performance in adverse environments. However, many existing methods (i) fuse modalities before sufficiently enhancing intra-modal semantics and (ii) are sensitive to modality discrepancies caused by heterogeneous sensor characteristics. To address these issues, we propose PACNet (Pyramid Attention Collaboration Network), a hierarchical RGB-T framework that jointly models multi-scale and global context and performs refinement-before-fusion with cross-modal collaboration. Specifically, Dense Atrous Spatial Pyramid Pooling (DASPP) captures multi-scale contextual cues... More >