Due to target diversity, life-cycle variations, and complex backgrounds, traditional pest detection methods often struggle with accuracy and efficiency. This study introduces RDW-YOLO, an improved pest detection algorithm based on YOLO11, featuring three key innovations. First, the Reparameterized Dilated Fusion Block (RDFBlock) enhances feature extraction via multi-branch dilated convolutions for fine-grained pest characteristics. Second, the DualPathDown (DPDown) module integrates hybrid pooling and convolution for better multi-scale adaptability. Third, an enhanced Wise-Wasserstein IoU (WWIoU) loss function optimizes the matching mechanism and improves bounding-box regression. Experiments on the enhanced IP102 dataset show that RDW-YOLO achieves an mAP@0.5 of 71.3% and an mAP@0.5:0.95 of 50.0%, surpassing YOLO11 by 3.1% and 2.0%, respectively. The model also adopts a lightweight design and has a computational complexity of 5.6 G, ensuring efficient deployment without sacrificing accuracy. These results highlight RDW-YOLO’s potential for precise and efficient pest detection in sustainable agriculture.

Two-dimensional human pose estimation aims to equip computers with the ability to accurately recognize human keypoints and comprehend their spatial contexts within media content. However, the accuracy of real-time human pose estimation diminishes when processing images with occluded body parts or overlapped individuals. To address these issues, we propose a method based on the YOLO framework. We integrate the convolutional concepts of Kolmogorov–Arnold Networks (KANs) through introducing non-linear activation functions to enhance the feature extraction capabilities of the convolutional kernels. Moreover, to improve the detection of small target keypoints, we integrate the cross-stage partial (CSP) approach and utilize the small object enhance pyramid (SOEP) module for feature integration. We also innovatively incorporate a layered shared convolution with batch normalization detection head (LSCB), consisting of multiple shared convolutional layers and batch normalization layers, to enable cross-stage feature fusion and address the low utilization of model parameters. Given the structure and purpose of the proposed model, we name it KSL-POSE. Compared to the baseline model YOLOv8l-POSE, KSL-POSE achieves significant improvements, increasing the average detection accuracy by 1.5% on the public MS COCO 2017 data set. Furthermore, the model also demonstrates competitive performance on the CrowdPOSE data set, thus validating its generalization ability.

Visible-infrared person re-identification (VI-ReID) task is to retrieve the same pedestrian across the visible and infrared modalities. The existing transformer-based works are constrained by the inherent structure of the ViT that feature collapse in deeper layers and the over-globalization of extracted features, resulting in incomplete learning of local and low-level features. However, these features are instrumental in representing and identifying elements within visible-infrared images more comprehensively, which increases the accuracy and robustness of cross-modal pedestrian matching. To solve the above problem, we propose the Local-Aware Residual Attention Vision Transformer (LAReViT) to enhance the learning of fine-grained local and shallow-level information to reinforce the feature discrimination and comprehensiveness in ViT. Specifically, the Local-Aware Residual (LAR) Module, which uses a novel Local Residual Attention (LRA) mechanism, is proposed to increase the fine-grained local information contained in feature extraction. In order to exploit fine-grained local information lost in lower-level visual features, the LRA in the LAR module adopts novel attention residual connections. Additionally, we propose a Positional Channel Reconstruction (PCR) Module that takes advantage of the local receptive field benefits of convolution. PCR reweights features within patches at the channel level, further facilitating the network emphasis on effective fine-grained local information. Finally, the novel Center Aggregation Loss (CAL) is designed to reduce modality discrepancies moderately and promote comprehensive feature extraction. Extensive experiments conducted on the SYSU-MM01, RegDB, and LLCM datasets demonstrate the state-of-the-art performance achieved by our proposed method. The code is available at https://github.com/Hua-XC/LAReViT .

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.

Accurate medical image segmentation can assist doctors in observing lesion areas and making precise judgments. Effectively utilizing important multi-scale semantic information in local and global contexts is key to improving segmentation accuracy. In this paper, we present a multi-scale dual attention network (MSDA-Net), which enhances feature representation under different receptive fields and effectively utilizes the important multi-scale semantic information from both local and global contexts in medical images. MSDA-Net is a typical encoder–decoder structure and introduces a multi-receptive field densely connected module (MRD) in the decoder. This module captures semantic information across various receptive fields and utilizes dense connections to provide comprehensive and detailed semantic representations. Furthermore, a parallel dual-branch attention module (PDA), incorporating spatial and channel attention, focuses intensively on detailed features within lesion areas. This module enhances feature representation, facilitates the identification of disease boundaries, and improves the accuracy of segmentation. To validate the effectiveness of MSDA-Net, we conducted performance analyses on the CVC-ClinicDB, 2018 Data Science Bowl, ISIC 2018, and colon cancer slice datasets. We also compared our method with U-Net, UNet++, and other methods. The experimental results unequivocally demonstrate that MSDA-Net outperforms these methods, showcasing its superior performance in medical image segmentation tasks.

The gradient vector flow (GVF) is an effective external force to deform the active contours. However, it suffers from high computational cost. For efficiency, the virtual electric field (VEF) model has been proposed, which can be implemented in real time thanks to fast Fourier transform (FFT). The VEF model has large capture range and low computation cost, but it has the limitations of sensitivity to noise and leakage on weak edge. The recently proposed CONVEF (Convolutional Virtual Electric Field) model takes the VEF model as a convolutional operation and employed another convolution kernel to overcome the drawbacks of the VEF model. In this paper, we employ an edge stopping function similar to that in the anisotropic diffusion to further improve the CONVEF model, and the proposed model is referred to as MCONVEF (Modified CONVEF) model. In addition, a piecewise constant approximation algorithm is borrowed to accelerate the calculation of the MCONVEF model. The proposed MCONVEF model is compared with the GVF and VEF models, and the experimental results are presented to demonstrate its superiority in terms of noise robustness, weak edge preserving and deep concavity convergence.