OEM-HWNet: A Prior Knowledge-Guided Network for Pavement Interlayer Distress Detection Based on Computer Vision Using GPR by Congde Lu, Senguo Cao, Xiao Wang, Guanglai Jin, Siqi Wang, Wenlong Cai

“…Finally, an additional detection head was added to improve the detection capability of interlayer distress with different sizes. Experiments demonstrated that the proposed network achieves a mean average precision (mAP) of 89.6%, outperforming other advanced models, such as YOLOv5s, YOLOv8s, YOLOv11s, and Faster R-CNN. …”
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PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection by Weijia Chen, Jiaming Liu, Tong Liu, Yaoming Zhuang

“…This module uses a parameter-aware mechanism to adapt its bottleneck structures to different network depths and widths, reducing parameters while maintaining performance. …”
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Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…Recently, most approaches do not explicitly capture the relationships between different solar wind features, and the prediction accuracy of 96‐hr is still not good enough. …”
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High-Accuracy Recognition Method for Diseased Chicken Feces Based on Image and Text Information Fusion by Duanli Yang, Zishang Tian, Jianzhong Xi, Hui Chen, Erdong Sun, Lianzeng Wang

“…Conventional visual-only methods face limitations due to environmental sensitivity and high visual similarity among feces from different diseases. To address this, we propose MMCD (Multimodal Chicken-feces Diagnosis), a ResNet50-based multimodal fusion model leveraging semantic complementarity between images and descriptive text to enhance diagnostic precision. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models.   …”
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Cross-Modal Object Detection Based on Content-Guided Feature Fusion and Self-Calibration by Liyang Ning, Xuxun Liu, Luoyu Zhou, Xueyu Zou

“…First, we introduce a parallel network in the backbone to enable the model to process information from different modalities simultaneously. Second, we design a content-guided fusion module (CGF) in the feature extraction network, leveraging both transformer and convolution operations to capture global and local information, thereby enhancing the model’s ability to focus on detailed object features. …”
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Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy by Tiandong Ma, Feng Li, Renlong Gao, Siyu Hu, Wenwen Ma

“…In this study, the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.…”
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AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics by Jian Feng, Lang Yu, Rui Ma

“…In the spatial dependency extraction module, according to the similarity of historical traffic flow sequences of different loop detectors, an adjacency matrix for the road network is constructed based on a sequence similarity calculation method, Predictive Power Score (PPS), to express latent spatial dependency; and then GCN is used on the adjacency matrix to capture the global spatial correlation and Transformer is used to capture dynamic spatial dependency from the most recently flow sequences. …”
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Joint Spectral Information and Spatial Details for Road Extraction From Optical Remote-Sensing Images by Yuzhun Lin, Jie Rui, Fei Jin, Shuxiang Wang, Xibing Zuo, Xiao Liu

“…A polarized self-attention mechanism was finally introduced at different levels of the fusion branch to reduce information loss during feature extraction, and operations, such as connected convolution and nonlinear activation, were later connected to complete the road prediction. …”
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A Novel Two-Level Protection Scheme against Hardware Trojans on a Reconfigurable CNN Accelerator by Zichu Liu, Jia Hou, Jianfei Wang, Chen Yang

“…With the boom in artificial intelligence (AI), numerous reconfigurable convolution neural network (CNN) accelerators have emerged within both industry and academia, aiming to enhance AI computing capabilities. …”
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PoseAlign network for hybrid structure in 2D human pose estimation by Jin Zhang, Yabo Yin, Wenzhong Yang, Doudou Ren, Danny Chen

“…Additionally, we observe varying contributions of different output features to the final performance. …”
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Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer by Denghui Liu, Lin Zhong, Haiyang Wu, Songyang Li, Yida Li

“…Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery. …”
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Distinguishing Difficulty Imbalances in Strawberry Ripeness Instances in a Complex Farmland Environment by Yang Gan, Xuefeng Ren, Huan Liu, Yongming Chen, Ping Lin

“…Firstly, a partial convolution-based compact inverted block is developed, which significantly enhances the feature extraction capability of the model. …”
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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

“…Secondly, the C3 module in the neck is replaced by the Res2Net module, which extracts features at different scales through multiple branches, not only ensuring rich details, but also enhancing the generalization ability of the network. …”
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Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning by Jinling Ren, Misheng Cai, Dapai Shi

“…The proposed method not only achieves high-precision SOH estimation among the same type of batteries but also demonstrates strong generalization ability under different battery chemistries and scenarios.…”
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Recognition Method of Corn and Rice Crop Growth State Based on Computer Image Processing Technology by Li Tian, Chun Wang, Hailiang Li, Haitian Sun

“…The fuzzy mathematical model is also devised to identify the characteristics of crops in different growth periods and to complete the identification of growth state. …”
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End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines by Karthikeyan Velayuthapandian, Natchiyar Murugan, Saranya Paramasivan

“…The experiment results for different individuals demonstrate an accuracy rate of around 99.84% in authenticating test samples. …”
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YOLO-WAD for Small-Defect Detection Boost in Photovoltaic Modules by Yin Wang, Wang Yun, Gang Xie, Zhicheng Zhao

“…Firstly, we replace C2f (CSP bottleneck with two convolutions) with C2f-WTConv (CSP bottleneck with two convolutions–wavelet transform convolution) in the backbone network to enlarge the receptive field and better extract the features of small-target defects (hot spots). …”
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Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning by Wen-Long Shang, Yanyan Chen, Xingang Li, Washington Y. Ochieng

“…In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. …”
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Cotton Weed-YOLO: A Lightweight and Highly Accurate Cotton Weed Identification Model for Precision Agriculture by Jinghuan Hu, He Gong, Shijun Li, Ye Mu, Ying Guo, Yu Sun, Tianli Hu, Yu Bao

“…The Receptive Field Enhancement (RFE) module is proposed to enable the feature pyramid network to adapt to the feature information of different receptive fields. A Scale-Invariant Shared Convolutional Detection (SSCD) head is proposed to fully utilize the advantages of shared convolution and significantly reduce the number of parameters in the detection head. …”
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