Vehicle detection method based on multi-layer selective feature for UAV aerial images by Yinbao Ma, Yuyu Meng, Jiuyuan Huo

“…In the backbone, a Receptive-Field Attention Convolution (RFAConv) module is introduced to retain detailed features during the downsampling process. …”
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Geometric properties of quantum entanglement and machine learning by S. V. Zuev

“…Fast data analysis based on hidden patterns is one of the main issues for adaptive artificial intelligence systems development. …”
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Deep learning-based text generation for plant phenotyping and precision agriculture by Li Zhu, Long Tang, Shan Ren, Shan Ren

“…An environment-aware module is included to address environmental variability.ResultsThe generative model uses advanced deep learning techniques to process high-dimensional imaging data, effectively capturing complex plant traits while overcoming issues like occlusion and variability. …”
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RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images by Zhenrong Deng, Xiaoming Li, Rui Yang

“…The approach introduces a tiered scale fusion feature (TSFF) module to enhance multi-scale detection accuracy by fusing features across network layers. …”
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Pedestrian Re-Identification Based on Fine-Grained Feature Learning and Fusion by Anming Chen, Weiqiang Liu

“…An MTLA consists of three modules, i.e., a multimodal feature encoder, token-based cross-modal alignment, and correlation-aware fusion. …”
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VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation by Guiwen Xu, Tao Hu, Qinghua Zhang

“…Secondly, the Vessel Dynamic Convolution (VDConv) module is designed to dynamically adapt to curved and crossing vessels, thereby improving the segmentation of complex morphologies. …”
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Multi-scale feature fusion keypoint detection network for ship draft line localization by Bo Zhang, Yumengmeng Yin, Kefu Ma, Hong Wang

“…Meanwhile, the Feature Enhancement Extraction Modules (FEEM) are employed to enhance these extracted features, and the Multi-scale Feature Weighted Integration (MFWI) module efficiently fuses the enhanced multi-scale features. …”
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ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images by Jing Zhang, Hao Zhou, Kunyu Liu, Yuguang Xu

“…Secondly, the DASPP (Deformable Atrous Spatial Pyramid Pooling) module was designed to use deformable atrous convolution to adaptively match the irregular boundaries of diseased areas, enhancing the model’s robustness to morphological variations caused by angles and occlusions in low-altitude drone photography. …”
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YOLOv8-TEA: Recognition Method of Tender Shoots of Tea Based on Instance Segmentation Algorithm by Wenbo Wang, Yidan Xi, Jinan Gu, Qiuyue Yang, Zhiyao Pan, Xinzhou Zhang, Gongyue Xu, Man Zhou

“…Firstly, this algorithm is based on the single-stage instance segmentation algorithm YOLOv8-seg, replacing some C2f modules in the original feature extraction network with MVB, combining the advantages of convolutional neural networks (CNN) and Transformers, and adding a C2PSA module following spatial pyramid pooling (SPPF) to integrate convolution and attention mechanisms. …”
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MCRFS-Net: single image dehazing based on multi-scale contrastive regularization and frequency selection by Qin Qin, Lin Shui, Yanyan Zhang, Shaojing Song, Jinhua Jiang

“…To overcome this limitation, this paper introduces a novel Adaptive Multi-Scale Frequency Selection (AMFS) module, which consists of an Adaptive Multi-Scale Module (AMSM) and a Frequency Selection Block (FSB). …”
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DelAwareCol: Delay Aware Collaborative Perception by Ahmed N. Ahmed, Siegfried Mercelis, Ali Anwar

“…Secondly, an inter-agent information aggregation module manages inter-agent interactions and spatial relationships, addressing common vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) issues, such as spatial misalignment, asynchronous information sharing, and pose errors. …”
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High-performance reconfigurable encryption scheme for distributed storage by Zhihua FENG, Yuxuan ZHANG, Chong LUO, Jianing WANG

“…As the world embraces the digital economy and enters an information society, data has emerged as a critical production factor.The collection, processing, and storage of data have become increasingly prevalent.Distributed storage systems, known for their efficiency, are widely used in various data fields.However, as the scale of data storage continues to expand, distributed storage faces more significant security risks, such as information leakage and data destruction.These challenges drive the need for innovative advancements in big data distributed storage security technology and foster the integration of domestic cryptographic technology with computing storage technology.This work focused on addressing security issues, particularly information leakage, in distributed storage nodes.A dynamic and reconfigurable encryption storage solution was proposed, which considered the requirements for encryption performance and flexibility.A high-performance reconfigurable cryptographic module was designed based on the bio mapping framework.Based on this module, multiple storage pools equipped with different cryptographic algorithms were constructed to facilitate high-performance encryption and decryption operations on hard disk data.The scheme also enabled dynamic switching of cryptographic algorithms within the storage pools.A cryptographic protocol with remote online loading functions for cryptographic algorithms and keys was developed to meet the unified management and convenient security update requirements of reconfigurable cryptographic modules in various storage nodes.Furthermore, the scheme implemented fine-grained data encryption protection and logical security isolation functions based on cryptographic reconstruction technology.Experimental results demonstrate that the performance loss of this scheme for encryption protection and security isolation of stored data is approximately 10%.It provides a technical approach for distributed storage systems to meet the cryptographic application technology requirements outlined in GB/T 39786-2021 “Information Security Technology-Basic Requirements for Cryptography Applications” Level 3 and above in terms of device and computing security, application and data security.…”
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Small-Target Detection Algorithm Based on STDA-YOLOv8 by Cun Li, Shuhai Jiang, Xunan Cao

“…Due to the inherent limitations of detection networks and the imbalance in training data, small-target detection has always been a challenging issue in the field of target detection. To address the issues of false positives and missed detections in small-target detection scenarios, a new algorithm based on STDA-YOLOv8 is proposed for small-target detection. …”
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AURA-Depth: Attention-Based Uncertainty Reduction and Feature Aggregation Depth Network by Youngtak Na, Sungho Woo, Soonyoung Lee, Byung Jun Kang

“…The output of each encoder branch’s residual module is fused using a Multimodal Cross Attention (MCA) module, guided by the uncertainty calculated from the absolute difference between dense depth features and triangulated depth features. …”
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GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

“…The lightweight feature extraction module C2f-GR is proposed to replace the C2f module. …”
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A novel pansharpening method based on cross stage partial network and transformer by Yingxia Chen, Huiqi Liu, Faming Fang

“…Finally, a residual learning module incorporating attention has been devised to augment the modeling and feature extraction capabilities of images. …”
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ACNet: An Attention–Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous Driving by Qiliang Zhang, Kaiwen Hua, Zi Zhang, Yiwei Zhao, Pengpeng Chen

“…Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. …”
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Enhancing, Refining, and Fusing: Towards Robust Multiscale and Dense Ship Detection by Congxia Zhao, Xiongjun Fu, Jian Dong, Shen Cao, Chunyan Zhang

“…CASS-Det integrates three key innovations: 1) a center enhancement module (CEM) that employs rotational convolution to emphasize ship centers, improving localization while suppressing background interference; 2) a neighbor attention module that leverages cross-layer dependencies to refine ship boundaries in densely populated scenes; and 3) a cross-connected feature pyramid network (CC-FPN) that enhances multiscale feature fusion by integrating shallow and deep features. …”
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EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement by Hu Haoyan, Tong Jinwu, Wang Haibin, Lu Xinyun

“…In response to the issues of low detection accuracy (DA), slow speed, and missed detections caused by the complex texture background and diverse shapes of surface defects (SD) in steel, this paper designs an improved lightweight YOLOv10 model called EAD-YOLOv10. …”
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A modality separation approach for facial sketch synthesis by Kangning Du, Chaoyi Wang, Lin Cao, Yanan Guo, Wenwen Sun

“…Next, an edge‐promoting module feeds processed blurry sketch images into the discriminator to enhance robustness. …”
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