In aerial images, slender and multi-scale insulator targets are often set against complex backgrounds, which imposes higher demands on the model's ability to retain details and capture long-range dependencies. To address this challenge, we propose a lightweight Dual-branch Residual Interaction Network (RI-Mamba) for insulator segmentation. Unlike typical single-branch architectures, RI-Mamba adopts a parallel dual-branch to prioritize the extraction of detail-semantic features. Specifically, the detail branch enhances the representation of local fine structures through feature expansion, while the semantic branch strengthens the correlation between positional and contextual semantics from a coordinate perspective. During the feature fusion stage, the proposed Residual Mamba Block (R-Mamba) discards traditional convolutional methods for processing local features and instead directly reuses detail-semantic information from the original features via a residual structure, achieving efficient feature reuse while avoiding parameter growth. Furthermore, a Gated Global-local Interaction Module (G2LIM) is introduced, which uses a learnable gated self-attention mechanism to dynamically adjust the fusion weights of global context and local details, thereby reducing redundant computation and enhancing feature adaptability. Experimental results on two public datasets demonstrate that RI-Mamba, with only 4.15M Params and 5.31G FLops, achieves mIoU of 86.28% and 91.59%, respectively, outperforming 17 lightweight insulator segmentation models. These results high ght that RI-Mamba achieves a favorable balance between computational efficiency and segmentation accuracy, making it well-suited for deployment in real-world power inspection scenarios. The code is available at: https://github.com/yaoshuang-yaobo/RI-Mamba.
Difference Visual Question Answering (Diff-VQA) in medical imaging automatically compares patient images across time points to support assessment of lesion progression and treatment efficacy. However, pixel-level matching is unreliable due to non-rigid deformations, view shifts, and acquisition noise, while existing models often rely on synthetic labels and lack effective integration of local and global information. To address these challenges, we propose a multimodal large-model framework that adopts a progressive “local semantic modeling–global difference reasoning” strategy. Key anatomical regions in chest X-rays are localized via object detection and aligned with VinDr-CXR annotations to construct region–disease mappings, transforming misalignment into semantic difference analysis. A dynamic sampling strategy further generates clinically meaningful image pairs with fine-grained difference labels. Finally, a multimodal large model fuses local features with global context to support single-image QA, dual-image disease description, and global difference reasoning. Experiments on the MIMIC-Diff-VQA dataset demonstrate state-of-the-art accuracy in single-image QA and substantial improvements in Diff-VQA tasks over mainstream medical large models. In the single-image QA tasks, our model improves accuracy from 52.5% to 64.1% (22.2% relative improvement), and in the Diff-VQA tasks, the CIDEr score increases from 1.027 to 1.379 (34.3% relative improvement). These results highlight the framework’s potential to enhance diagnostic accuracy and strengthen clinical decision support in radiology practice.
Printed Circuit Boards (PCB) are a fundamental component of electronic devices, serving to connect and support electronic components. They are widely used in various fields, including Consumer Electronics, Industrial Control, and Aerospace. However, modern PCBs are trending toward miniaturization, lightweight design, and high density. Consequently, tiny surface defects appear more frequently. Defects such as missing holes, mouse bites, and open circuits exhibit low contrast, making them challenging to detect. Traditional inspection methods suffer from several limitations. Manual visual inspection and electrical testing are inefficient. They suffer from low accuracy, poor efficiency, and high costs. The automated optical inspection (AOI) still needs people’s assistance. In addition, the early deep learning models like the initial versions of YOLO do not accurately detect small defects. During upsampling, these models lose fine-grained details and fail to adapt to unbalanced data. Consequently, they often fail to detect low-contrast defects. To address these issues, this paper proposes YOLO-iDAT. It is an improved version of YOLOv11. First, this paper changes the C2PSA module into C2PSA_iRMB. This new module combines the iRMB structure, SE attention and residual connections. It makes the model mix the global and local information better. And it significantly enhances the ability to extract the tiny defect feature. Second, this paper uses DySample for up sampling. It is a dynamic method. It prevents the blurring of small defects that often occurs with conventional upsampling methods. It keeps the feature clear. And it does not make the model slow. Third, this paper uses the Adaptive Threshold Focal Loss (ATFL) function. It uses an adaptive threshold and adjusts parameters to adapt to unbalanced data. It significantly reduces the number of missed defects. This paper uses the proposed model to test the public PKU-Market-PCB dataset. The results show that YOLO-iDAT gets a precision of 0.941, the mAP50 of 0.93 and the recall of 0.895. These represent improvements of 0.9%, 1%, and 1.8%, respectively, over the original YOLOv11. And the speed of our model is very fast. It gets FPS of 88.76. This is a sufficient speed for real-time detection in factory settings. These results demonstrate that YOLO-iDAT fits the requirement of industrial PCB defect detection. It provides a more accurate and more efficient solution for PCB defect detection.
As a core interaction in the human-vehicle-road system, automated and refined detection of driving behavior has emerged as a crucial research direction in intelligent transportation systems and advanced driver assistance systems. Traditional post-event monitoring models that rely on manual or sensor-based methods are no longer able to meet the requirements of real-time and accurate risk identification. Therefore, this study proposes the You Only Look Once - Lightweight - BiFPN - ECA (YOLO-LBE) detection method. By integrating ghost convolution and GhostC2f modules to diminish computational complexity, the study employs a weighted bidirectional feature pyramid network, and further embed an ECA module to significantly enhance the precision and stability of driver behavior detection. Experimental findings demonstrate that the improved YOLOv8 model improves mAP@0.5 by 5.3%, FPS by 32.4%, Params by 34.4%, and FLOPs by 33.3% compared to YOLOv5s. This research method outperforms existing mainstream models in terms of accuracy, efficiency, and interference tolerance, providing reliable technical support for real-time driving behavior monitoring.
The continuous expansion of the scale of the IoT has led to frequent occurrence of network attack events and posed severe challenges to device operation and data security. To accurately track the behavior of IoT devices in the network and detect any malicious activities that may occur, this study proposes a spatiotemporal feature-based IoT security detection method that combines TPA mechanism, BiLSTM, DenseNet 1D, and CNN. The experiment was validated using two publicly available datasets, UNSW-NB15 and Bot-IoT. The research method could effectively identify various types of attacks (such as user to administrator privilege escalation attacks, remote to local attacks, etc.), alleviate the impact of imbalanced dataset categories, and maintain low online detection latency. In the UNSW-NB15, the precision, recall, and F1 value of the research method reached 97.6%, 96.8%, and 97.2%, while in the Bot-IoT, they reached 95.7%, 94.1%, and 94.9%. When the sample size increased to 1×106, the running time and energy consumption of the research method were only 13.2 s and 0.9 kWh. The research method effectively enhances the ability to identify multiple types of attack traffic, providing a technical solution that balances accuracy and robustness for malicious behavior detection in IoT environments.
