Video surveillance on the offshore booster station and around the coast is a effective way to monitor floating macroalgae. Previous studies on floating algae detection are mainly based on traditional image segmentation methods. However, these algorithms cannot effectively solve the problem of extracting Ulva prolifra and Sargassum at different sizes and views. Recently, instance segmentation methods have achieved great success in computer vision applications. In this paper, based on the CenterMask network, a novel instance segmentation architecture named AlgaeMask is proposed for floating algae detection from the surveillance videos. To address the feature extraction ability of the network in the inter-dependencies for position and channel, we introduce a new OSA-V3 module with the dual-attention block, which consists of a position attention mechanism and channel attention mechanism. Meanwhile, scale-equalizing pyramid convolution is introduced to solve the problem of scale difference. Finally, we introduce the feature decoder module based on FCOS head and segmentation head to obtain the segmentation area of floating algae in each bounding box. The extensive experiment results show that the average precision of our AlgaeMask in the tasks of mask segmentation and box detection can reach 44.22% and 48.13%, respectively, which has 15.09% and 8.24% improvement over CenterMask. In addition, the AlgaeMask can meet the real-time requirements of floating algae detection.

The detection of Cerebral Microbleeds (CMBs) is crucial for diagnosing cerebral small vessel disease. However, due to the small size and subtle appearance of CMBs in susceptibility-weighted imaging (SWI), manual detection is both time-consuming and labor-intensive. Meanwhile, the presence of similar-looking features in SWI images demands significant expertise from clinicians, further complicating this process. Recently, there has been a significant advancement in automated detection of CMBs using a Convolutional Neural Network (CNN) structure, aiming at enhancing diagnostic efficiency for neurologists. However, existing methods still show discrepancies when compared to the actual clinical diagnostic process. To bridge this gap, we introduce a novel multimodal detection and classification framework for CMBs’ diagnosis, termed MM-UniCMBs. This framework includes a light-weight detection model and a multi-modal classification network. Specifically, we proposed a new CMBs detection network, CMBs-YOLO, designed to capture the salient features of CMBs in SWI images. Additionally, we design an innovative language–vision classification network, CMBsFormer (CF), which integrates patient textual descriptions—such as gender, age, and medical history—with image data. The MM-UniCMBs framework is designed to closely align with the diagnostic workflow of clinicians, offering greater interpretability and flexibility compared to existing methods. Extensive experimental results show that MM-UniCMBs achieves a sensitivity of 94% in CMBs’ classification and can process a patient’s data within 5 s.

Real-time monitoring of offshore wind turbines (OWTs) through satellite remote sensing imagery is considered an essential process for large-scale infrastructure surveillance in ocean engineering. Current detection systems, however, are constrained by persistent technical limitations, including prohibitive deployment costs, insufficient discriminative power for learned features, and susceptibility to environmental interference. To address these challenges, a dual-branch architecture named OWTDNet is proposed, which integrates global contextual modeling via State Space Models (SSMs) with CNN-based local feature extraction for high-resolution OWTs detection. The primary branch utilizes a Mamba-structured encoder with linear computational complexity to establish long-range spatial dependencies, while an auxiliary Blurring-MobileNetv3 (B-Mv3) branch is designed to compensate for the local feature extraction deficiencies inherent in SSMs. Additionally, a novel Feature Alignment Module (FAM) is introduced to systematically coordinate cross-modal feature fusion between Mamba and CNN branches through channel-wise recalibration and position-aware alignment mechanisms. This module not only enables complementary feature integration but also enhances turbine-specific responses through attention-driven feature modulation. Comprehensive experimental validation demonstrated the superiority of the proposed framework, achieving a mean average precision (AP) of 47.1% on 40,000 × 40,000-pixel satellite imagery, while maintaining practical computational efficiency (127.7 s per image processing time).