The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm’s precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method’s superiority and efficacy.

Forest fires result in severe disaster, causing significant ecological damage and substantial economic losses. Flames and smoke represent the predominant characteristics of forest fires. However, these flames and smoke often exhibit irregular shapes, rendering them susceptible to erroneous positive or negative identifications, consequently compromising the overall performance of detection systems. To enhance the average precision and recall rates of detection, this paper introduces an enhanced iteration of the You Only Look Once version 5 (YOLOv5) algorithm. This advanced algorithm aims to achieve more effective fire detection. First, we use Switchable Atrous Convolution (SAC) in the backbone network of the traditional YOLOv5 to enhance the capture of a larger receptive field. Then, we introduce Polarized Self-Attention (PSA) to improve the modeling of long-range dependencies. Finally, we incorporate Soft Non-Maximum Suppression (Soft-NMS) to address issues related to missed detections and repeated detections of flames and smoke by the algorithm. Among the plethora of models explored, our proposed algorithm achieves a 2.0% improvement in mean Average Precision@0.5 (mAP⁵⁰) and a 3.1% enhancement in Recall when compared with the YOLOv5 algorithm. The integration of SAC, PSA, and Soft-NMS significantly enhances the precision and efficiency of the detection algorithm. Moreover, the comprehensive algorithm proposed here can identify and detect key changes in various monitoring scenarios.

The accurate detection of occluded tomatoes in complex greenhouse environments remains challenging due to the limited feature representation ability and high computational costs of existing models. This study proposes SWMD-YOLO, a lightweight multi-scale detection network optimized for greenhouse scenarios. The model integrates switchable atrous convolution (SAConv) and wavelet transform convolution (WTConv) for the dynamic adjustment of receptive fields for occlusion-adaptive feature extraction and to decompose features into multi-frequency sub-bands, respectively, thus preserving critical edge details of obscured targets. Traditional down-sampling is replaced with a dynamic sample (DySample) operator to minimize information loss during resolution transitions, while a multi-scale convolutional attention (MSCA) mechanism prioritizes discriminative regions under varying illumination. Additionally, we introduce Focaler-IoU, a novel loss function that addresses sample imbalance by dynamically re-weighting gradients for partially occluded and multi-scale targets. Experiments on greenhouse tomato data sets demonstrate that SWMD-YOLO achieves 93.47% mAP50 with a detection speed of 75.68 FPS, outperforming baseline models in accuracy while reducing parameters by 18.9%. Cross-data set validation confirms the model’s robustness to complex backgrounds and lighting variations. Overall, the proposed model provides a computationally efficient solution for real-time crop monitoring in resource-constrained precision agriculture systems.

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products.

Snow on the Tibetan Plateau (TP) is not only a freshwater resource for the major rivers in Asia but also plays a significant role in adjusting temperature by reflecting solar radiation. Fractional snow cover mapping with fine spatial and temporal resolution is of great significance for clarifying snow resources and accurately managing snow water resources. However, due to the complex TP topography, the existing FSC retrieval methods are affected by a variety of disturbance factors, resulting in a decrease in accuracy. In this study, a deep learning-based FSC retrieval method, fractional snow cover U-shape net (FSC-USNet), is proposed to improve snow cover mapping accuracy. The input images of FengYun-4A Advanced Geosynchronous Radiation Imager (FY4A/AGRI) and geographic data are extracted using the proposed spatial-channel feature extraction module to characterize the shallow features with texture information into high-dimensional feature images. Additionally, an attention mechanism is introduced to improve the feature differences between different FSC degrees. Finally, the correlation of the decoded features in the channel direction is mined using the channel refinement module to obtain the final FSC results. In this study, a backpropagation artificial neural network (BPANN), random forest, ResNet_FSC, and UNet are trained, compared and validated against the MOA10A1 FSC product. The results show that the proposed method effectively mitigates the problems of unclear texture of snow edges, poor robustness and underestimation in some areas, which exist in other models. Additionally, the proposed method has higher accuracy, with an R2 and explained variance score (EVS) reaching 0.7182 and 0.7332, respectively. Compared to the MOD10A1 snow product, the proposed method exhibits higher detection accuracy in mountainous areas with high snow cover and significantly improves the low snow cover detection rate.

The use of satellite remote sensing for rapid and accurate monitoring of water bodies can provide holistic and dynamic information on water resources, which is of great importance for the prevention and control of floods, droughts, and other disasters. Optical satellite remote sensing data can be affected by cloudy weather, which leads to the problem of large errors in water body identification. We use Sentinel-1 data to construct a synthetic aperture radar water body dataset and propose MAG-Net, a network model that fuses multi-level and multi-scale information. The model structure consists of a multi-scale residual block as the backbone network, which is designed to enhance the ability to capture different scale features of water bodies. The ability to capture detailed information is also enhanced by introducing a global information fusion block. Finally, the decoder is designed as a full-size hop-connected structure, which improves the accuracy of narrow water body recognition by combining feature maps at different scales. Experiments show that the extraction accuracy of the proposed model on Sentinel-1 data and the generalization ability on GaoFen-3 (GF-3) data outperform the existing water body extraction models. MAG-Net achieved impressive results on the Sentinel-1 dataset, with an accuracy of 97.09%, recall of 97.50%, and F1-score of 97.25%. These scores represent significant improvements of 1.89%, 2.94%, and 2.43%, respectively, compared with the worst-performing models. In addition, MAG-Net demonstrated a high accuracy rate of 95.63%, a recall rate of 94.46%, and an F1-score of 94.18% on the GF-3 verification set.