Monitoring ships on water surfaces encounters obstacles such as weather conditions, sunlight, and water ripples, posing significant challenges in accurately detecting target ships in real time. Synthetic Aperture Radar (SAR) offers a viable solution for real-time ship detection, unaffected by cloud coverage, precipitation, or light levels. However, SAR images are often affected by speckle noise, salt-and-pepper noise, and water surface ripple interference. This study introduces LCAS-DetNet, a Multi-Location Cross-Attention Ship Detection Network tailored for the ships in SAR images. Modeled on the YOLO architecture, LCAS-DetNet comprises a feature extractor, an intermediate layer (“Neck”), and a detection head. The feature extractor includes the computation of Multi-Location Cross-Attention (MLCA) for precise extraction of ship features at multiple scales. Incorporating both local and global branches, MLCA bolsters the network’s ability to discern spatial arrangements and identify targets via a cross-attention mechanism. Each branch utilizes Multi-Location Attention (MLA) and calculates pixel-level correlations in both channel and spatial dimensions, further combating the impact of salt-and-pepper noise on the distribution of objective ship pixels. The feature extractor integrates downsampling and MLCA stacking, enhanced with residual connections and Patch Embedding, to improve the network’s multi-scale spatial recognition capabilities. As the network deepens, we consider this structure to be cascaded and multi-scale, providing the network with a richer receptive field. Additionally, we introduce a loss function based on Wise-IoUv3 to address the influence of label quality on the gradient updates. The effectiveness of our network was validated on the HRSID and SSDD datasets, where it achieved state-of-the-art performance: a 96.59% precision on HRSID and 97.52% on SSDD.

Electrochemiluminescence (ECL) is a phenomenon in which light is emitted when an electrochemical reaction occurs in a solution. ECL images are widely used in various fields. However, ECL images often carry considerable unavoidable noise. Prior to using ECL images, denoising it is very important. At present, the simplest and most valuable image denoising method is the center adaptive median filter (CAMF). However, in some images, CAMF does not work very well. There are three main kinds of images: images with excessive noise areas in the light spot, images with large differences in the size of the light spot, and images with different shapes of light spots. To make the CAMF problems easier to solve, we narrow the decision criteria for the current pixels inside and outside the spot, the selection of the filter size threshold, the selection of the nearest spot, the determination of the spot center, etc., and we then propose CAMFv2. Our experiments show that our CAMFv2 is better than CAMF.

Recent advances in deep learning have significantly enhanced the accuracy of poultry image recognition, particularly in assessing poultry conditions. However, developing intuitive decision support tools remain a significant challenge. To address this, we present PoulTrans, an innovative image captioning framework that leverages a Convolutional Neural Network (CNN) integrated with a CSA_Encoder-Transformer architecture to generate detailed poultry status reports. This model incorporates visual features extracted by CNNs into the Channel Spatial Attention Segmentation Encoder (CSA_Encoder), which produces segmented channel and spatial attention outputs. To optimize multi-level attention and improve the semantic precision of the status descriptions, we introduced a Channel Spatial Memory-Guided Transformer (CSMT) and a novel PS-Loss function. The performance of PoulTrans was tested on the PSC-Captions dataset, achieving top scores of 0.501, 0.803, 4.927, 0.608, and 1.882 for the BLEU-4, ROUGE-L, CIDEr, SPICE, and Sm metrics, respectively. Comprehensive analyses and experiments have validated the effectiveness and reliability of our model, providing advanced tools for automated poultry status generation and enhancing the digital experience for poultry farmers. Our code is available at: https://github.com/kong1107800/PoulTrans.

Accurate classification of poultry behavior is critical for assessing welfare and health, yet most existing methods predict behavior categories without providing explanations for the image content. This study introduces the PBC-Transformer model, a novel model that integrates image captioning techniques to enhance poultry behavior classification, mimicking expert assessment processes. The model employs a multi-head concentrated attention mechanism, Head Spatial Position Coding (HSPC), to enhance spatial information; a learnable sparse mechanism (LSM) and RNorm function to reduce noise and strengthen feature correlation; and a depth-wise separable convolutional network for improved local feature extraction. Furthermore, a multi-level attention differentiator dynamically selects image regions for precise behavior descriptions. To balance caption generation with classification, we introduce the ICL-Loss function, which adaptively adjusts loss weights. Extensive experiments on the PBC-CapLabels dataset demonstrate that PBC-Transformer outperforms 13 commonly used classification models, improving accuracy by 15% and achieving the highest scores across image captioning metrics: Bleu4 (0.498), RougeL (0.794), Meteor (0.393), and Spice (0.613).

Current research on tea primarily focuses on foundational studies of phenotypic characteristics, with insufficient exploration of the relationship between tea varieties and suitable populations. To address this issue, this paper proposes a link prediction model based on Graph Neural Networks (GNN) and tensor decomposition, named GNN-FTuckER, designed to predict the “tea suitability” relationships within the tea knowledge graph. This model integrates the SE-GNN structural encoder with an improved TuckER model decoder. The SE-GNN encoder enhances the modeling capability of the global graph structure by explicitly modeling relations, entities, and triples, thereby obtaining embedding vectors through aggregation, updating, and iterative operations. The improved TuckER model enhances the capture of complex semantics between entities and relations by introducing nonlinear activation functions. To support our research, we constructed a tea dataset, TeaPle. In comparative experiments, GNN-FTuckER achieved superior performance on both public datasets (WN18RR, FB15k-237) and the TeaPle dataset. Ablation studies indicate that the model improved H@10 by 4.3% on the WN18RR dataset and by 1.5% on the FB15k-237 dataset, with a 1.3% increase in MRR. In the TeaPle dataset, H@3 improved by 4.7% and H@10 increased by 3.1%. This research provides significant insights for further exploring the potential of tea varieties and evaluating the health benefits of tea consumption.