A Lightweight Citrus Object Detection Method in Complex Environments by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li, Jie Zhou

“…Aiming at the limitations of current citrus detection methods in complex orchard environments, especially the problems of poor model adaptability and high computational complexity under different lighting, multiple occlusions, and dense fruit conditions, this study proposes an improved citrus detection model, YOLO-PBGM, based on You Only Look Once v7 (YOLOv7). …”
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Retinal vascular segmentation network based on dual-scale morphological enhancement by Yunfeng Ni, Pei Wang, Wei Chen, Jie Qi

“…Subsequently, a feature aggregation module based on dynamic snake-shaped convolutions is introduced, which adapts convolution paths to achieve the fusion of features at different levels. …”
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Evaluation of Dose Calculation Algorithms Accuracy for ISOgray Treatment Planning System in Motorized Wedged Treatment Fields by Sajjad Raghavi, Hamid-Reza Sadoughi, Mohammad Ehsan Ravari, Marziyeh Behmadi

“…Background: Different dose calculation methods vary in accuracy and speed. …”
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Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT by Mengying Xiong, Aiping Wu, Yue Yang, Qingqing Fu

“…The D-LKA mechanism is introduced into the C3k2 structure, and the large convolution kernel is used to process complex image information to enhance the model’s ability to characterize different scales and irregular tumors. …”
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A Segmentation Network with Two Distinct Attention Modules for the Segmentation of Multiple Renal Structures in Ultrasound Images by Youhe Zuo, Jing Li, Jing Tian

“…Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. …”
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L-ENet: An Ultralightweight SAR Image Detection Network by Yutong Wang, Min Miao, Shiliang Zhu

“…Furthermore, the trihead detection structure is revised to a dual-head structure omni-dimensional adaptive spatial feature fusion, using object detection convolution to allocate weights across different scales for efficient detection. …”
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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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Capsule Endoscopy Image Enhancement for Small Intestinal Villi Clarity by Shaojie Zhang, Yinghui Wang, Peixuan Liu, Yukai Wang, Liangyi Huang, Mingfeng Wang, Ibragim Atadjanov

“…Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. …”
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Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…To achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, as well as fruit occlusion. …”
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Deep learning for predicting myopia severity classification method by WangMeiYu Xing, XiaoNa Li, JingShu Ni, YuanZhi Zhang, ZhongSheng Li, Yong Liu, YiKun Wang, Yao Huang

“…To improve the efficiency of myopia screening, this paper proposes a deep learning model, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to classify different severities of myopia. …”
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Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis by Yisheng Chen, Yu Xiao, Hui Wu, Chongcheng Chen, Ding Lin

“…The MSA module extracts features through multi-scale convolutions and adaptively aggregates features, focusing on both local geometric details and global semantic information at different scale levels, enhancing the understanding of complex scenes. …”
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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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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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An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms by Yanbin Weng, Jie Yang, Changfan Zhang, Jing He, Cheng Peng, Lin Jia, Hui Xiang

“…Secondly, the receptive field is enlarged by cascading atrous convolutions with different dilation rates in the ASPP (atrous spatial pyramid pooling) module, and other feature maps are concatenated using the multi-scale attention module to enhance the extraction accuracy of the model. …”
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DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments by Jun Zhang, Dongfang Zhang, Jingyan Liu, Yuhong Zhou, Xiaoshuo Cui, Xiaofei Fan

“…Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. …”
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DMM-YOLO: A high efficiency soil fauna detection model based on an adaptive dynamic shuffle mechanism by Jiehui Ke, Renbo Luo, Guoliang Xu, Yuna Tan, Zhifeng Wu, Liufeng Xiao

“…Additionally, different efficient modules with multi-branch fusion structures, integrating DLSConv, are adopted for the Backbone and Neck to enhance feature extraction and fusion, while optimizing the feature maps fed into the detection head, thereby improving the network’s ability to extract features and detect targets. …”
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Multiscale Edge Enhancement and Progressive Change-Aware Network for Remote Sensing Change Detection by Yan Xing, Jiali Hu, Yunan Jia, Rui Huang

“…In addition, we propose a progressive change-aware module that progressively applies depthwise separable convolutions with kernels of decreasing size to localize changes at different scales, enabling precise refinement of change objects and reducing pseudochanges. …”
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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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