Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Infrared small target detection (IRSTD) is the process of separating targets from complex and blurry backgrounds, with wide applications in rescue, maritime guidance, and other fields. However, traditional deep learning methods that rely solely on CNN-based methods are prone to losing targets as the convolution deepens, resulting in missed detections when segmenting small infrared targets. Similarly, because of the unique characteristics of infrared small targets, Transformer-based methods lack the supplement of convolutional local detail features, making it difficult to achieve good results. To address these issues, we propose the Feature Adaptive Transformer and CNN network (FATCNet): a dual branch decoder structure network that utilizes CNN and Transformer to obtain local and global features in parallel. Meanwhile, we propose the Attention Guided Adaptive Fusion Block (AGAF) to better integrate global and local feature information, eliminate semantic gaps, and dynamically fuse features based on network learning. The network encoder incorporates the Hierarchical Attention Module (HAM), which leverages the distinct characteristics of various hierarchical feature maps to enhance features, thereby mitigating the risk of losing crucial details pertaining to infrared small targets. Additionally, We have constructed a bird infrared dataset, named BSIRST v2, which comprises 1000 authentic images with diverse target shapes, sizes, and cluttered backgrounds, and the dataset includes meticulous pixel-level annotations. Our approach's efficacy has been validated through experiments conducted on both public datasets and BSIRST v2. In comparison to other state-of-the-art methods, our FATCNet outperforms in terms of probability of detection (PD), F1 score (F1), and intersection over union (IoU).

Infrared small target detection (IRSTD) has significant value in many civilian and military applications. However, most current methods still struggle to maintain tiny targets with low contrast and are prone to false alarms caused by confusing noise similar to the targets. Furthermore, challenges remain in accurately segmenting target shapes due to the difficulty in acquiring sufficient fine-grained details. To address these issues, we propose the AER-Net to extract discriminative features of small targets while leveraging the distinct semantics of different layers for reducing false alarms and preserving edge contours of targets. Specifically, we present a U-shaped architecture tailored for IRSTD by a Feature Representation Enhancement Module (FREM), which combines the local correlation of CNNs and the global correlation of Transformers to enhance features while preventing the loss of tiny targets. Based on this, we further devise a Daisy-shaped Correlation-aware Module (DCM) to capture sufficient global contexts for mitigating clutter interference in deeper layers. Subsequently, a Gated Refinement Module (GRM) is designed to refine low-level features from the encoder utilizing high-level features as guidance for preserving local details and further removing noise in shallower layers. Moreover, we integrate our method with the monitoring device to extend the application of IRSTD to automatic monitoring of birds in plateau lakes and wetlands, while contributing an infrared bird dataset named BSIRST V1 with targets exhibiting high morphological uncertainty to advance research in this field. Extensive experiments on three public and the proposed datasets have demonstrated that our method achieves a balance between detection capability and segmentation accuracy. Notably, our approach has significant improvements in mean IoU metric on the challenging benchmark NUDT-SIRST and IRSTD-1k datasets with 4.46% and 3.86%, respectively. The source code and dataset are available at https://github.com/fuqingzhang/AER-Net.

Ensuring personal safety and preventing accidents are critical aspects of power construction safety supervision. However, current monitoring methods are inefficient and unreliable as most of them rely on manual monitoring and transmission, which results in slow detection and delayed warnings regarding violations. To overcome these challenges, we propose an intelligent detection system that can accurately identify instances of illegal wearing of power construction workers in real-time. Firstly, we integrated the squeeze-and-excitation (SE) module into our convolutional neural network to enhance detection accuracy. This module effectively prioritizes informative features while suppressing less relevant ones, resulting in improved overall performance. Secondly, we present an embedded real-time detection system that utilizes Jetson Xavier NX and Edge-YOLO. This system promptly detects and alerts power construction workers of instances of illegal wearing behavior. To ensure a lightweight implementation, we design appropriate detection heads based on target size and distribution, reducing model parameters while enhancing detection speed and minimizing accuracy loss. Additionally, we employed data augmentation to enhance the system’s robustness. Our experimental results demonstrate that our improved Edge-YOLO model achieves high detection precision and recall rates of 0.964 and 0.966, respectively, with a frame rate of 35.36 frames per second when deployed on Jetson Xavier NX. Therefore, Edge-YOLO proves to be an ideal choice for intelligent real-time detection systems, providing superior accuracy and speed performance compared to the original YOLOv5s model and other models in the YOLO series for safety monitoring at construction sites.

In order to quickly and accurately detect whether a chef is wearing a hat and mask, a kitchen standard dress detection method based on the YOLOv5s embedded model is proposed. Firstly, a complete kitchen scene dataset was constructed, and the introduction of images for the wearing of masks and hats allows for the low reliability problem caused by a single detection object to be effectively avoided. Secondly, the embedded detection system based on Jetson Xavier NX was introduced into kitchen standard dress detection for the first time, which accurately realizes real-time detection and early warning of non-standard dress. Among them, the combination of YOLOv5 and DeepStream SDK effectively improved the accuracy and effectiveness of standard dress detection in the complex kitchen background. Multiple sets of experiments show that the detection system based on YOLOv5s has the highest average accuracy of 0.857 and the fastest speed of 31.42 FPS. Therefore, the proposed detection method provided strong technical support for kitchen hygiene and food safety.

Infrared small target detection (IRSTD) plays a critical role in both civilian and military applications, yet it still faces inherent challenges stemming from faint targets, complex noise interference, and difficulties in preserving shape integrity. Despite significant progress in detecting general small targets, existing methods often struggle to balance detection accuracy and false alarms due to limited sensitivity to low-intensity signals, inaccurate perception of confusing noise, and inadequate edge refinement. To break this dilemma, we propose ISGLNet, which centers on a U-shaped architecture specifically tailored to preserve salient target responses, along with a guided learning strategy that progressively enhances target–noise distinction while refining boundary details. Specifically, we introduce the Context-aware Local-Global Module (CLGM) as the cornerstone of the model, which incorporates multi-branch large receptive fields and multi-dimensional adaptive attention mechanisms, effectively capturing rich contexts while preserving critical target information. This ensures reliable feature modeling throughout the extraction and fusion process. Furthermore, the Multi-frequency Perception Module (MFPM) and the Edge Refinement Module (ERM) replace conventional skip connections to refine semantic patterns through guidance. Among these, the MFPM operates in the deeper layers, primarily identifying discriminative clues by evaluating and dynamically selecting multi-frequency information to amplify the distinction between targets and complex noise. The ERM further works in the shallower layers with a progressive strategy to refine uncertain target boundaries, enabling precise segmentation of fine-grained target shapes. Extensive experiments on multiple public datasets demonstrate that ISGLNet achieves superior performance in both detection and segmentation accuracy. The code is available at https://github.com/fuqingzhang/ISGLNet.