DCD-FPI: A Deformable Convolution-Based Fusion Network for Unmanned Aerial Vehicle Localization by Yufei He, Feiyang Chen, Jiahao Chen, Jiqi Fan, Enhui Zheng

“…Moreover, our model reduces computational complexity from 14.28 GFLOPS to 11.54 GFLOPS and parameter quantity from 14.76 M to 13.96 M.…”
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Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm by Shun Cheng, Zhiqian Wang, Shaojin Liu, Yan Han, Pengtao Sun, Jianrong Li

“…Firstly, the SPDConv module is utilized in the backbone network to replace the standard convolutional module for feature extraction. This enhances computational efficiency and reduces redundant computations. …”
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Low-Damage Grasp Method for Plug Seedlings Based on Machine Vision and Deep Learning by Fengwei Yuan, Gengzhen Ren, Zhang Xiao, Erjie Sun, Guoning Ma, Shuaiyin Chen, Zhenlong Li, Zhenhong Zou, Xiangjiang Wang

“…The lightweight network Mobilenet is used as the feature extraction network to reduce the number of parameters of the network. …”
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Individual-based multiscale model for foot-and-mouth disease by Blessings Mufoya, Dephney Mathebula, Dephney Mathebula, Winston Garira

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Data Integration Based on UAV Multispectra and Proximal Hyperspectra Sensing for Maize Canopy Nitrogen Estimation by Fuhao Lu, Haiming Sun, Lei Tao, Peng Wang

“…However, the CE of the integrated model decreased by 1.93% and 1.68%, respectively. Key features included multispectral red-edge indices (NREI, NDRE, CI) and texture parameters (R1m), alongside hyperspectral indices (SR, PRI) and spectral parameters (SDy, Rg) exhibited varying directional impacts on CNC estimation using RF. …”
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Contour wavelet diffusion – a fast and high-quality facial expression generation model by Chenwei Xu, Yuntao Zou

“…Latent space diffusion models have shown promise in speeding up training by leveraging feature space parameters, but they require additional network structures. …”
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CAFNet: Cross-Modal Adaptive Fusion Network With Attention and Gated Weighting for RGB-T Semantic Segmentation by Meili Fu, Huanliang Sun, Zhihan Chen, Lulin Wei

“…The experimental results show that CAFNet achieves a 60.1% mIoU on the MFNet dataset, which is 1.2% higher than that of EAEFNet (58.9% mIoU), and the computational cost (110.61G FLOPs) and parameter count (68.13 M) are also reduced by 25% and 66.1%, respectively. …”
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YOLO-GCOF: A Lightweight Low-Altitude Drone Detection Model by Wanjun Yu, Kongxin Mo

“…The YOLO-GCOF model outperforms the original YOLOv8n, as demonstrated by a 1.1% improvement in mAP@50, alongside reductions in parameter count, computational overhead, and model size by 60%, 49.4%, and 55.1%, respectively. …”
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LI-YOLOv8: Lightweight small target detection algorithm for remote sensing images that combines GSConv and PConv. by Pingping Yan, Xiangming Qi, Liang Jiang

“…In the domain of remote sensing image small target detection, challenges such as difficulties in extracting features of small targets, complex backgrounds that easily lead to confusion with targets, and high computational complexity with significant resource consumption are prevalent. …”
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CGDINet: A Deep Learning-Based Salient Object Detection Algorithm by Chengyu Hu, Jianxin Guo, Hanfei Xie, Qing Zhu, Baoxi Yuan, Yujie Gao, Xiangyang Ma, Jialu Chen, Juan Tian

“…The results show that CGDINet outperforms other mainstream significance object detection models in evaluation metrics such as <inline-formula> <tex-math notation="LaTeX">${\mathrm {maxF}}_{\mathrm {\beta }}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\mathrm {S}_{\mathrm {\alpha }}$ </tex-math></inline-formula>, and MAE, with almost no increase in computational cost (FLOPs) and parameters. The experimental results validate that CGDINet can effectively address the issues of incomplete global feature extraction and insufficient attention to key areas, thereby significantly enhancing the performance of significance object detection.…”
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Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk Hα Images by GaoFei Zhu, GangHua Lin, Xiao Yang, Cheng Zeng

“…Each block effectively optimizes the channel features from the previous layer, significantly reducing parameters. …”
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YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…Secondly, a C2f_RVB module is designed, which uses the RepViTBlock technique to optimize feature representation and effectively reduce the number of model parameters. …”
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Research on Lightweight Method of Insulator Target Detection Based on Improved SSD by Bing Zeng, Yu Zhou, Dilin He, Zhihao Zhou, Shitao Hao, Kexin Yi, Zhilong Li, Wenhua Zhang, Yunmin Xie

“…The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. …”
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Fusion of Multimodal Audio Data for Enhanced Speaker Identification Using Kolmogorov-Arnold Networks by Aryaman Tamotia, Dhruv Shantu Karmokar, Rethi Komal, K. Khadar Nawas, A. Shahina, A. Nayeemulla Khan

“…Although the classical deep learning methods are effective, they have rather high computational cost, which leads to usually cumbersome parameter tuning processes and hence reduce their applicability to real-world deployments. …”
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A lightweight large receptive field network LrfSR for image super-resolution by Wanqin Wang, Shengbing Che, Wenxin Liu, Yangzhuo Tuo, Yafei Du, Zixuan Zhang

“…However, existing methods often suffer from issues such as large number of parameters, intensive computation, and high latency, which limit the application of deep convolutional neural networks on devices with low computational resources. …”
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PPLIO: Plane-to-Plane LiDAR-Inertial Odometry With Multi-View Constraint in Real-Time by Hanyeol Lee, Jae Hyung Jung, Won Young Chung, Suyong Lee, Chan Gook Park

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Multi-Strategy Improvement of Coal Gangue Recognition Method of YOLOv11 by Hongjing Tao, Lei Zhang, Zhipeng Sun, Xinchao Cui, Weixun Yi

“…It exhibits a slight increase in computational load, despite an almost unchanged number of parameters, and demonstrates the best overall detection performance. …”
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LWheatNet: a lightweight convolutional neural network with mixed attention mechanism for wheat seed classification by Xiaojuan Guo, Jianping Wang, Guohong Gao, Zihao Cheng, Zongjie Qiao, Ranran Zhang, Zhanpeng Ma, Xing Wang

“…Each network consists of three core layers, with each core layer is comprising one downsampling unit and multiple basic units. To minimize model parameters and computational load without sacrificing performance, each unit utilizes depthwise separable convolutions, channel shuffle, and channel split techniques.ResultsTo validate the effectiveness of the proposed model, we conducted comparative experiments with five classic network models: AlexNet, VGG16, MobileNet V2, MobileNet V3, and ShuffleNet V2. …”
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A global object-oriented dynamic network for low-altitude remote sensing object detection by Daoze Tang, Shuyun Tang, Yalin Wang, Shaoyun Guan, Yining Jin

“…This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. …”
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Transcriptome Derived Artificial neural networks predict PRRC2A as a potent biomarker for epilepsy by Wayez Naqvi, Prekshi Garg, Prachi Srivastava

“…It aids clinicians in addressing patient parameters and translational research. Artificial neural networks (ANNs) are computer models that attempt to mimic the neurons present in the human brain. …”
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