Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings by Jing Gu, Yijuan Pan, Jingjing Zhang

“…In practice, this improvement has led to more accurate maintenance and safety assessments of concrete buildings and earlier detection of potential structural problems, resulting in lower maintenance costs and longer building life. This not only improves the safety of buildings but also brings significant economic benefits and social value to the industries involved.…”
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An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction by Josue Lopez-Cabrejos, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque

“…In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. …”
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Analysis of Time-Fractional Delay Partial Differential Equations Using a Local Radial Basis Function Method by Kamran, Kalsoom Athar, Zareen A. Khan, Salma Haque, Nabil Mlaiki

“…The aim of utilizing the Laplace transform is to handle the costly convolution integral associated with the Caputo derivative and to avoid the effects of time-stepping techniques on the stability and accuracy of the numerical solution. …”
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MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking by Jianyu Xie, Yan Fu, Junlin Zhou, Tianxiang He, Xiaopeng Wang, Yuke Fang, Duanbing Chen

“…The fully connected head network module addresses the shortcomings of traditional convolutional head networks such as inductive biases. …”
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Self-Supervised Learning of End-to-End 3D LiDAR Odometry for Urban Scene Modeling by Shuting Chen, Zhiyong Wang, Chengxi Hong, Yanwen Sun, Hong Jia, Weiquan Liu

“…While deep learning offers solutions, current approaches for point cloud alignment exhibit key limitations: self-supervised losses often neglect inherent alignment uncertainties, and supervised methods require costly pixel-level correspondence annotations. To address these challenges, we propose UnMinkLO-Net, an end-to-end self-supervised LiDAR odometry framework. …”
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AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net by Ming Zhao, Yimin Yang, Bingxue Zhou, Quan Wang, Fu Li

“…These results illustrate that the method proposed in this paper not only excels in the segmentation of complex shapes and small targets but also significantly enhances overall performance at lower computational costs. This research offers new insights and references for model design in future medical image segmentation tasks.…”
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Infrared image super resolution with structure prior from uncooled infrared readout circuit by Kai Che, Jian Lv, Jia Wei, Jiayuan Gong, Longcheng Que, Yun Zhou

“…Building on these components, we propose a hybrid network named Efficient Infrared Image Super-Resolution (EIRSR), which achieves an excellent balance between performance and efficiency in terms of parameters and computational costs. Furthermore, we scale the model to create a family of variants, collectively known as EIRSRs. …”
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Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11 by Lei Zhang, Zhipeng Sun, Hongjing Tao, Meng Wang, Weixun Yi

“…The proposed improvements are realized through three key aspects: Firstly, the traditional convolution is replaced with GSConv, which significantly enhances feature extraction capabilities while simultaneously reducing computational costs. …”
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DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades by Li Zou, Anqi Chen, Chunzi Li, Xinhua Yang, Yibo Sun

“…Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…ObjectiveUAVs provide advantages such as easy control, low cost, and good performance, and efficiently perform tasks in diverse sites and complex environments. …”
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Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM by Xiao Lai, Guanglong Fu

“…At the stereo matching level, we enhance the SGBM (Semi-Global Block Matching) algorithm through improved cost calculation and cost aggregation, resulting in Opti-SGBM (Optimized SGBM). …”
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A lightweight remote sensing image detection model with feature aggregation diffusion network by Xiaohui Cheng, Xukun Wang, Yun Deng, Qiu Lu, Yanping Kang, Jian Tang, Yuanyuan Shi, Junyu Zhao

“…To address this, we propose LightFAD-YOLO, a lightweight model integrating feature aggregation diffusion for multi-scale context propagation, enhancing small object detection in complex scenes. The central convolutional detection head combines detail-enhanced convolution and group normalization, reducing computational costs by 23.4 % while maintaining precision. …”
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Efficient Side-Tuning for Remote Sensing: A Low-Memory Fine-Tuning Framework by Haichen Yu, Wenxin Yin, Hanbo Bi, Chongyang Li, Yingchao Feng, Wenhui Diao, Xian Sun

“…To reduce memory requirements and training costs, this article proposes a low-memory fine-tuning framework, called efficient side-tuning (EST), for remote sensing downstream tasks. …”
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FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection by Yiwen Cui, Xu Han, Jiaying Chen, Xinguang Zhang, Jingyun Yang, Xuguang Zhang

“…We propose a novel GNN architecture, Temporal-Spatial-Semantic Graph Convolution (TSSGC), which simultaneously captures temporal patterns, spatial relationships, and semantic information in transaction data. …”
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FCDNet: A Lightweight Network for Real-Time Wildfire Core Detection in Drone Thermal Imaging by Linfeng Wang, Oualid Doukhi, Deok Jin Lee

“…However, as the demand for higher accuracy in detection algorithms grows, the detection model’s size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection. …”
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LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

“…Finally, we design a new detection head, CLLAHead, which reduces computational costs and strengthens the robustness of the model through cross-layer local attention. …”
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Mastitis Classification in Dairy Cows Using Weakly Supervised Representation Learning by Soo-Hyun Cho, Mingyung Lee, Wang-Hee Lee, Seongwon Seo, Dae-Hyun Lee

“…Therefore, this study proposed a mastitis classification based on weakly supervised representation learning using an autoencoder on time series milking data, which allows for concurrent milking representation learning and weakly supervision with low-cost labels. The proposed method employed a structure where the classifier branches from the latent space of a 1D-convolutional autoencoder, enabling representation learning of milking data to be conducted from the perspective of reconstructing the original information and detecting mastitis. …”
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Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT by Peng Shen, Keyu Mei, Haori Xue, Tenglong Li, Guoqing Zhang, Yongxiang Zhao, Wei Luo, Liang Mao

“…Currently, manual counting is inefficient, costly, and unsuitable for systematic analysis. However, research on dynamic pig counting encounters challenges, including inadequate detection accuracy stemming from crowding, occlusion, deformation, and low-light conditions. …”
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A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks by Xiaoning Zhang, Zhaoyang Peng, Yifei Wang, Fan Ye, Tengying Fu, Hu Zhang

“…In response to these issues, this paper proposes a novel and robust multispectral reconstruction network from low-cost natural color RGB images based on free available satellite images with various land cover types. …”
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Multimode Flex-Interleaver Core for Baseband Processor Platform by Rizwan Asghar, Dake Liu

“…By exploiting the hardware reuse methodology the silicon cost is reduced, and it consumes 0.126 mm2 area in total in 65 nm CMOS process for a fully reconfigurable architecture. …”
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