In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its precision in detection, this study initially introduces ConvNeXtV2 and Conv2Former to the You Only Look Once version 7 (YOLOv7) algorithm, separately, and then compares the results with the original YOLOv7 algorithm through experiments. After comprehensive comparison, the proposed ConvNeXtV2-YOLOv7 based on ConvNeXtV2 exhibits a superior performance in detecting forest fires. Additionally, in order to further focus the network on the crucial information in the task of detecting forest fires and minimize irrelevant background interference, the efficient layer aggregation network (ELAN) structure in the backbone network is enhanced by adding four attention mechanisms: the normalization-based attention module (NAM), simple attention mechanism (SimAM), global attention mechanism (GAM), and convolutional block attention module (CBAM). The experimental results, which demonstrate the suitability of ELAN combined with the CBAM module for forest fire detection, lead to the proposal of a new method for forest fire detection called CNTCB-YOLOv7. The CNTCB-YOLOv7 algorithm outperforms the YOLOv7 algorithm, with an increase in accuracy of 2.39%, recall rate of 0.73%, and average precision (AP) of 1.14%.

Forest fires pose a severe threat to ecological environments and the safety of human lives and property, making real-time forest fire monitoring crucial. This study addresses challenges in forest fire image object detection, including small fire targets, sparse smoke, and difficulties in feature extraction, by proposing TFNet, a Transformer-based multi-scale feature fusion detection network. TFNet integrates several components: SRModule, CG-MSFF Encoder, Decoder and Head, and WIOU Loss. The SRModule employs a multi-branch structure to learn diverse feature representations of forest fire images, utilizing 1 × 1 convolutions to generate redundant feature maps and enhance feature diversity. The CG-MSFF Encoder introduces a context-guided attention mechanism combined with adaptive feature fusion (AFF), enabling effective multi-scale feature fusion by reweighting features across layers and extracting both local and global representations. The Decoder and Head refine the output by iteratively optimizing target queries using self- and cross-attention, improving detection accuracy. Additionally, the WIOU Loss assigns varying weights to the IoU metric for predicted versus ground truth boxes, thereby balancing positive and negative samples and improving localization accuracy. Experimental results on two publicly available datasets, D-Fire and M4SFWD, demonstrate that TFNet outperforms comparative models in terms of precision, recall, F1-Score, mAP50, and mAP50–95. Specifically, on the D-Fire dataset, TFNet achieved metrics of 81.6% precision, 74.8% recall, an F1-Score of 78.1%, mAP50 of 81.2%, and mAP50–95 of 46.8%. On the M4SFWD dataset, these metrics improved to 86.6% precision, 83.3% recall, an F1-Score of 84.9%, mAP50 of 89.2%, and mAP50–95 of 52.2%. The proposed TFNet offers technical support for developing efficient and practical forest fire monitoring systems.

Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters.

Point-of-interest (POI) recommendation is highly sensitive to temporal factors, including fluctuations in user preferences, variation in user similarity, and decay in the attraction of locations. However, current studies overlook the temporal dynamics of user similarity and the timeliness of POIs, resulting in a disconnect between recommendations and users’ recent preferences. This paper proposes a new framework for dynamic and timely POI recommendation by integrating spatio-temporal influences and social relationships. Dynamic prediction is achieved through an enhanced user-based collaborative filtering approach. A time slot clustering technique was designed based on the statistical check-in features in each time slot. Ratings within the same cluster were shared to address data sparsity. To reflect user similarity drift, we took the time variable as a crucial parameter to dynamically calculate user similarity. Moreover, timely prediction was achieved by integrating the timeliness, popularity, and spatial features of POIs. We introduce a novel method to evaluate the timeliness of POI recommendation, aimed at assessing whether the recommendations align with users’ recent preferences. Comprehensive experiments are performed on Brightkite and Gowalla datasets, with the data divided into workdays and weekends. The experimental results reveal that our algorithm outperforms seven state-of-the-art recommenders in terms of prediction accuracy and system timeliness.

Object detection in UAV aerial imagery plays a pivotal role across a wide spectrum of applications. However, existing detection models continue to face significant challenges stemming from small object scales, dense spatial distributions, and highly complex backgrounds. To address these challenges, this paper proposes a novel dual-backbone network model named WCDB-YOLO. The core innovation of this work lies in introducing a “target-context decoupled perception” paradigm, which utilizes two structurally complementary backbone networks to separately process local object features and global background information: one backbone focuses on extracting fine-grained local features of objects, while the other innovatively incorporates a wavelet convolution module to efficiently model the global contextual semantics of complex scenes with minimal computational cost by constructing a large receptive field. To further enhance the scale adaptability for small objects, a Dilation-wise Residual (DWR) module is designed, which employs parallel convolutional branches with different dilation rates to achieve dynamic adaptation to multi-scale small object features. Additionally, the model optimizes the feature pyramid structure by integrating high-resolution P2/4 features into the detection head, significantly improving the localization accuracy of tiny objects. Experimental results on the VisDrone dataset show that the proposed method achieves an 8.4% improvement in mAP50 over the baseline YOLOv11s model and outperforms current state-of-the-art (SOTA) approaches. This work presents a highly accurate and robust solution for small object detection from UAV platforms in complex environments.

Underwater visual technology is crucial for marine surveys, but challenges such as low contrast, blurriness, uneven lighting, and noise degrade image quality. We propose generative adversarial network with ResUNet++ and convolutional block attention module (RUC-GAN), a generative adversarial network architecture that employs a ResUNet++-based generator enhanced with convolutional block attention modules (CBAMs). The proposed architecture incorporates CBAM attention mechanisms within the ResUNet++ encoder structure, achieving complementary benefits; the attention modules provide adaptive feature refinement, whereas the underlying ResUNet++ architecture maintains its effective hierarchical feature representation capabilities. When evaluated on the underwater image enhancement benchmark (UIEB) dataset, RUC-GAN outperforms existing methods, achieving a peak signal-to-noise ratio (PSNR) of 25.34, a structural similarity index measure (SSIM) of 0.91, and a feature similarity index measure (FSIM) of 0.95 on the test set. These results represent a significant improvement of at least 1.93 PSNR, 0.10 SSIM, and FSIM of 0.05 over classical algorithms. RUC-GAN demonstrates superior performance in preserving image details, reducing noise, and maintaining realistic colors.