YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…This method employs variable input parameters to directly calculate key point distances between predicted and ground-truth boxes, more accurately reflecting positional differences between detection results and reference targets, thus effectively improving the model’s mean Average Precision (mAP). …”
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Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…However, DEM data and general images have unique characteristics and fundamental differences, making it inappropriate to directly apply image-based super-resolution methods to extract terrain features. …”
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Depth-Enhanced Tumor Detection Framework for Breast Histopathology Images by Integrating Adaptive Multi-Scale Fusion, Semantic Depth Calibration, and Boundary-Guided Detection by A. Robert Singh, Suganya Athisayamani, Hariharasitaraman S, Faten Khalid Karim, Jose Varela-Aldas, Samih M. Mostafa

“…DG-TDM refines tumor boundary detection by combining depth and RGB features using spatial and channel-wise attention mechanisms, which highlight boundary differences and reduce redundancy from background noise. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…The introduction of the Focal-EIOU loss function calculates the width and height differences of target bounding boxes and uses Focal Loss to address the imbalance of difficult and easy samples, achieving faster convergence and superior localization capability. …”
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A Multitask Network for the Diagnosis of Autoimmune Gastritis by Yuqi Cao, Yining Zhao, Xinao Jin, Jiayuan Zhang, Gangzhi Zhang, Pingjie Huang, Guangxin Zhang, Yuehua Han

“…The endoscopic manifestations of AIG differ from those of gastritis caused by <i>Helicobacter pylori</i> (<i>H. pylori</i>) infection in terms of the affected gastric anatomical regions and the pathological characteristics observed in biopsy samples. …”
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Multi-scale cross-layer fusion and center position network for pedestrian detection by Qian Liu, Youwei Qi, Cunbao Wang

“…Pedestrian detection has made breakthroughs after the rise of convolutional neural networks. However, it faces some challenging problems, including dataset difference, small pedestrian targets and occlusions between pedestrians. …”
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High-Precision Qiantang River Water Body Recognition Based on Remote Sensing Image by Hongcui Wang, Yihong Zheng, Ouxiang Chen

“…River water body identification plays an important role in flood monitoring, urban planning, Thus, it attracts more interests of studying and investigating, especially based on remote sensing technology, The traditional NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) methods are widely used, However, these methods need manual intervention to select the threshold, In order to achieve automatic water body recognition, deep learning methods, such as CNN, VGG, Unet etc., are applied, Currently there are few works on the water body identification of Qiantang River, Here, one major challenge for high-precision Qiantang water body recognition is the real complex water body features and complicated geological environment, They are the dense distribution of small water bodies in the Qiantang River Basin, large differences in water body nutrition, and the high complexity of surface environments such as mountains and plains, We investigated two traditional and several deep learning methods and found that WatNet was the most effective model for Qiantang River, This model adopts the structure based on encoder-decoder convolutional network, It uses MobileNetV2 as the encoder, which makes it extract more water feature information while being lightweight and uses ASPP module to capture global multi-scale features in deep layers, Experimental results show that the MIoU and OA (Overall Accuracy) can reach 0. 97 and 0. 99 respectively.…”
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Ulnar variance detection from radiographic images using deep learning by Sahar Nooh, Abdelrahim Koura, Mohammed Kayed

“…Abstract Ulnar variance is a relative length difference in the wrist between the ulna and radius bones. …”
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Deep learning models for deriving optimised measures of fat and muscle mass from MRI by Belvin Thomas, M. Adam Ali, Fatima M. H. Ali, Anthony Chung, Manjiri Joshi, Sophia Maiguma-Wilson, Gabrielle Reiff, Hadil Said, Pardis Zalmay, Michael Berks, Matthew D. Blackledge, James P. B. O’Connor

“…There was negligible difference in the accuracy of human observers and all deep learning models in delineating SF or EM. …”
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Diagnosis and activity prediction of SLE based on serum Raman spectroscopy combined with a two-branch Bayesian network by Qianxi Xu, Qianxi Xu, Qianxi Xu, Xue Wu, Xue Wu, Xue Wu, Xinya Chen, Ziyang Zhang, Jinrun Wang, Jinrun Wang, Zhengfang Li, Zhengfang Li, Zhengfang Li, Xiaomei Chen, Xiaomei Chen, Xiaomei Chen, Xin Lei, Xin Lei, Zhuoyu Li, Zhuoyu Li, Zhuoyu Li, Mengsi Ma, Mengsi Ma, Mengsi Ma, Chen Chen, Lijun Wu, Lijun Wu

“…DBayesNet is primarily composed of a two-branch structure, with features at different levels extracted by the Bayesian Convolution (BayConv) module, Attention module, and finally, feature fusion performed by Concate, which is performed by the Bayesian Linear Layer (BayLinear) output to obtain the result of the classification prediction.ResultsThe two sets of Raman spectral data were measured in the spectral wave number interval from 500 to 2000 cm-1. …”
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Polarimetric SAR Ship Detection Using Context Aggregation Network Enhanced by Local and Edge Component Characteristics by Canbin Hu, Hongyun Chen, Xiaokun Sun, Fei Ma

“…Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. …”
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Root Cause Analysis of Cast Product Defects with Two-Branch Reasoning Network Based on Continuous Casting Quality Knowledge Graph by Xiaojun Wu, Xinyi Wang, Yue She, Mengmeng Sun, Qi Gao

“…However, reasoning schemes for general KGs often use the same processing method to deal with different types of relations, without considering the difference in the number distribution of the head and tail entities in the relation, leading to a decrease in reasoning accuracy. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…Ultimately, high-quality difference images are generated from the extracted bi-temporal features, then use thresholding analysis to obtain a final change map. …”
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Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) by Leonardo Volpato, Evan M. Wright, Francisco E. Gomez

“…The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…For this purpose, we conducted forest type classification at three different levels, including 1. forest and nonforest, 2. hardwood, softwood, and nonforest, and 3. three dominant forest types, including paper birch, black spruce, white spruce, and nonforest. …”
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An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection by Chengsheng Yuan, Zhenyu Xu, Xinting Li, Zhili Zhou, Junhao Huang, Ping Guo

“…Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. …”
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Application of Deep Learning in Forest Fire Prediction: A Systematic Review by Cesilia Mambile, Shubi Kaijage, Judith Leo

“…Key meteorological features, such as Temperature, Humidity, and Wind speed, have been extensively studied using the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Moisture Index (NDMI), the most commonly used satellite-derived features. …”
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Optimizing the automated recognition of individual animals to support population monitoring by Tijmen A. deLorm, Catharine Horswill, Daniella Rabaiotti, Robert M. Ewers, Rosemary J. Groom, Jessica Watermeyer, Rosie Woodroffe

“…To evaluate intraspecific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat coloration patterns. The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. …”
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Non-stationary signal combined analysis based fault diagnosis method by Zhe CHEN, Yuqi HU, Shiqing TIAN, Huimin LU, Lizhong XU

“…Considering the complementarity between the deep learning,spectrum and time frequency analysis methods,a multi-stream framework was designed by combining the convolutional network,Fourier transform and wavelet package decomposition methods,with the aim to analyze the non-stationary signal.Accordingly,a none-stationary signal combined analysis based fault diagnosis method was proposed to extract features in difference aspects.The fault diagnosis experiments demonstrate that the combined analysis method can efficiently and stably depict the fault and significantly improve the performance of fault diagnosis.…”
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Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China by Zhuo Chen, Tao Liu

“…A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background.…”
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