An enhanced network for extracting tunnel lining defects using transformer encoder and aggregate decoder by Bo Guo, Zhihai Huang, Haitao Luo, Perpetual Hope Akwensi, Ruisheng Wang, Bo Huang, Tsz Nam Chan

“…We propose a deep network model utilizing an encoder–decoder framework that integrates Transformer and convolution for comprehensive defect extraction. …”
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An Intelligent Field Monitoring System Based on Enhanced YOLO-RMD Architecture for Real-Time Rice Pest Detection and Management by Jiangdong Yin, Jun Zhu, Gang Chen, Lihua Jiang, Huanhuan Zhan, Haidong Deng, Yongbing Long, Yubin Lan, Binfang Wu, Haitao Xu

“…This integrated solution addresses the dual requirements of precision and timeliness in field monitoring, representing a significant advancement for agricultural vision systems. The developed framework provides practical implementation pathways for precision pest management under real-world farming conditions.…”
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MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging by Yusheng Wu, Qiang Lin, Yang He, XianWu Zeng, Yongchun Cao, ZhengXing Man, Caihong Liu, Yusheng Hao, Zhengqi Cai, Jinshui Ji, Xiaodi Huang

“…Methods We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. …”
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YOLOv8-RBean: Runner Bean Leaf Disease Detection Model Based on YOLOv8 by Hongbing Chen, Haoting Zhai, Jinghuan Hu, Hongrui Chen, Changji Wen, Yizhe Feng, Kun Wang, Zhipeng Li, Guangyao Wang

“…To address this issue, this study proposes an improved detection model, YOLOv8_RBean, based on the YOLOv8n object detection framework, specifically designed for runner bean leaf disease detection. …”
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EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images by Zhenyu Liu, Xianjun Gao, Yuanwei Yang, Lei Xu, Shaoning Wang, Ningsheng Chen, Zhiwei Wang, Yuan Kou

“…Integrating efficient receptive field expansion convolution into lightweight network models facilitated efficient feature extraction. …”
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Polara-Keras2c: Supporting Vectorized AI Models on RISC-V Edge Devices by Nizar El Zarif, Mohammadhossein Askari Hemmat, Theo Dupuis, Jean-Pierre David, Yvon Savaria

“…While traditional AI frameworks are powerful, they often fall short in meeting the requirements of edge computing, such as low latency, constrained computational power, and energy efficiency. …”
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Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…The experiments conducted on moderate resolution imaging spectroradiometer NDVI data show that the NIT-Net framework is superior to most of the comparison methods. …”
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PC3D-YOLO: An Enhanced Multi-Scale Network for Crack Detection in Precast Concrete Components by Zichun Kang, Kedi Gu, Andrew Yin Hu, Haonan Du, Qingyang Gu, Yang Jiang, Wenxia Gan

“…To address these limitations, we propose PC3D-YOLO, an enhanced framework derived from YOLOv11, which strengthens long-range dependency modeling through multi-scale feature integration, offering a novel approach for crack detection in precast concrete structures. …”
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SFDA-MEF: An Unsupervised Spacecraft Feature Deformable Alignment Network for Multi-Exposure Image Fusion by Qianwen Xiong, Xiaoyuan Ren, Huanyu Yin, Libing Jiang, Canyu Wang, Zhuang Wang

“…Therefore, we propose an unsupervised learning framework for the multi-exposure fusion of optical spacecraft image sequences. …”
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Surface Defect and Malformation Characteristics Detection for Fresh Sweet Cherries Based on YOLOv8-DCPF Method by Yilin Liu, Xiang Han, Longlong Ren, Wei Ma, Baoyou Liu, Changrong Sheng, Yuepeng Song, Qingda Li

“…To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R² of 0.980988. …”
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Research Advances in Underground Bamboo Shoot Detection Methods by Wen Li, Qiong Shao, Fan Guo, Fangyuan Bian, Huimin Yang

“…To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network–transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. …”
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MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery by Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan

“…First, we reconstruct the ResNet-18 backbone network using deep separable convolutions, reducing computational complexity while preserving feature representation capabilities. …”
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SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase by Yu Zhao, Fei Liu, Qiang He, Fang Liu, Xiaohu Sun, Jiyong Zhang

“…To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. …”
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Early Breast Cancer Prediction Using Thermal Images and Hybrid Feature Extraction-Based System by Doaa Youssef, Hanan Atef, Shaimaa Gamal, Jala El-Azab, Tawfik Ismail

“…These findings show the significant advantages of the proposed framework for early prediction of breast cancer using thermal images.…”
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Implementation for Lightweight Deep Learning for Anomaly Detection and Denoising on Gravitational Waves by R. K. Mohith Niranjen, C. Yogesh, Anirudh Vinodh, Tharun Sureshkumar, S. Vatchala

“…Motivated by the WaveNet model, our method uses dilated convolutions to precisely model long-term dependencies in the data ensuring that subtle characteristics are captured. …”
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FP-Deeplab: A Novel Face Parsing Network for Fine-Grained Boundary Detection and Semantic Understanding by Borui Zeng, Can Shu, Ziqi Liao, Jingru Yu, Zhiyu Liu, Xiaoyan Chen

“…To address these challenges, this paper proposes a facial parsing network based on the Deeplabv3+ framework, named FP-Deeplab, which aims to improve segmentation performance and generalization capability through structurally enhanced modules. …”
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DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles by Siqiang Cheng, Lingshan Chen, Kun Yang

“…To address these issues, this paper introduces DGSS-YOLOv8s, a model designed to enhance detection accuracy and high-FPS performance within the You Only Look Once version 8 small (YOLOv8s) framework. The key innovation lies in the synergistic integration of several architectural enhancements: the DCNv3_LKA_C2f module, leveraging Deformable Convolution v3 (DCNv3) and Large Kernel Attention (LKA) for better the capture of complex object shapes; an Optimized Feature Pyramid Network structure (Optimized-GFPN) for improved multi-scale feature fusion; the Detect_SA module, incorporating spatial Self-Attention (SA) at the detection head for broader context awareness; and an Inner-Shape Intersection over Union (IoU) loss function to improve bounding box regression accuracy. …”
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Efficient Identification and Classification of Pear Varieties Based on Leaf Appearance with YOLOv10 Model by Niman Li, Yongqing Wu, Zhengyu Jiang, Yulu Mou, Xiaohao Ji, Hongliang Huo, Xingguang Dong

“…YOLOv10 based on the PyTorch framework was applied to train the leaf dataset, and construct a pear leaf identification and classification model. …”
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Green Apple Detection Method Based on Multidimensional Feature Extraction Network Model and Transformer Module by Wei Ji, Kelong Zhai, Bo Xu, Jiawen Wu

“…To enhance the fast and accurate detection of pollution-free green apples for food safety, this paper uses the DETR network as a framework to propose a new method for pollution-free green apple detection based on a multidimensional feature extraction network and Transformer module. …”
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