Geometry-Constrained Learning-Based Visual Servoing with Projective Homography-Derived Error Vector by Yueyuan Zhang, Arpan Ghosh, Yechan An, Kyeongjin Joo, SangMin Kim, Taeyong Kuc

“…The fixed-dimension error function ensures a constant Jacobian size regardless of the number of feature points, thereby reducing computational complexity. …”
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The Lightweight Method of Ground Penetrating Radar (GPR) Hidden Defect Detection Based on SESM-YOLO by Yu Yan, Guangxuan Jiao, Minxing Cui, Lei Ni

“…The model also shows improvements in precision (92.4%) and recall (86.7%), with reductions in parameters and computational load, demonstrating significant advantages over current mainstream detection models.…”
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Research on lightweight malware classification method based on image domain by SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie

“…This mechanism guided the model to focus on more informative features, while depthwise separable convolution further reduced the number of model parameters. …”
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Multiscale Convolutional Fusion Network for Efficient Monaural Speech Separation by Rui Yang, Shanliang Pan

“…Finally, cross-scale modulation upsampling efficiently reconstructs the acoustic features. Experiments on three datasets demonstrate that our method achieves state-of-the-art performance among lightweight speech separation models, with low computational complexity and fast inference. …”
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Multi-Vehicle Object Recognition Method Based on YOLOv7-W by Xin Liu, Jingbin Zhao, Zhi Zhang, Rongxing Wu, Xun Li

“…Analysis of the XUPEI-CAR experimental dataset reveals significant variations in features across different traffic flow densities. To improve the matching accuracy of prior frames, the k-means++ clustering algorithm is employed to optimize the prior frame parameters. …”
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MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…Furthermore, compared with multiple latest UAV object detection algorithms, our designed MSUD-YOLO offers higher detection accuracy and lower computational cost; e.g., mAP50 reaches 43.4%, but parameters are only 6.766 M.…”
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Lightweight remote sensing change detection with progressive multi scale difference aggregation by Yinghua Fu, Haifeng Peng, Tingting Zhao, Yize Li, Jiansheng Peng, Dawei Zhang

“…However, many previous neural network-based approaches require a large number of parameters and computations and high-performance hardware, which makes their practical application in remote sensing challenging. …”
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SAG-YOLO: A Lightweight Real-Time One-Day-Old Chick Gender Detection Method by Yulong Chang, Rongqian Sun, Zheng Yang, Shijun Li, Qiaohua Wang

“…Firstly, the model reduces both parameter size and computational complexity by replacing the original feature extraction with the StarNet lightweight Backbone. …”
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A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis by Jiamao Yu, Hexuan Hu

“…However, current deep learning-based methods face significant challenges, particularly due to the scarcity of fault data, which impedes the models’ ability to effectively learn parameters. Additionally, many existing methods rely on single-scale features, hindering the capture of global contextual information and diminishing diagnostic accuracy. …”
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Synergistic Multi-Model Approach for GPR Data Interpretation: Forward Modeling and Robust Object Detection by Hang Zhang, Zhijie Ma, Xinyu Fan, Feifei Hou

“…Ground penetrating radar (GPR) is widely used for subsurface object detection, but manual interpretation of hyperbolic features in B-scan images remains inefficient and error-prone. …”
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Analysis and modeling of digital solutions in medical database management by Rakhimov Bakhtiyar, Rakhimova Feruza, Saidov Atabek, Saidova Zarina

“…This high accuracy is attributed to expanded Haar-like feature templates, efficient computation using integral images, and a comprehensive contour feature extraction process. …”
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Optimized classification of potato leaf disease using EfficientNet-LITE and KE-SVM in diverse environments by Gopal Sangar, Velswamy Rajasekar

“…EfficientNet-LITE improves the model's emphasis on pertinent features through Channel Attention (CA) and 1-D Local Binary Pattern (LBP), while preserving computational economy with a reduced model size of 12.46 MB, fewer parameters at 3.11M, and a diminished FLOP count of 359.69 MFLOPs.ResultsBefore optimization, the SVM classifier attained an accuracy of 79.38% on uncontrolled data and 99.07% on laboratory-controlled data. …”
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FDM-RTDETR: A Multi-Scale Small Target Detection Algorithm by Hongya Wang, Yongtao Yu, Zhaoxia Tang

“…While maintaining low computational complexity and parameter count, the method demonstrates significant improvements in detection performance. …”
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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

“…First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. …”
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REDalign: accurate RNA structural alignment using residual encoder-decoder network by Chun-Chi Chen, Yi-Ming Chan, Hyundoo Jeong

“…In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. …”
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Identifying time patterns in Huntington’s disease trajectories using dynamic time warping-based clustering on multi-modal data by Alexia Giannoula, Audrey E. De Paepe, Ferran Sanz, Laura I. Furlong, Estela Camara

“…From a wide range of examined user-defined parameters, four case examples are highlighted to demonstrate the identified temporal patterns in multi-modal HD trajectories and to study how these differ due to the combined effects of feature weights and granularity threshold. …”
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FruitQuery: A lightweight query-based instance segmentation model for in-field fruit ripeness determination by Ziang Zhao, Yulia Hicks, Xianfang Sun, Chaoxi Luo

“…Efficient multi-head self-attention modules are introduced to the backbone to reduce computational overhead, and a pyramid pooling module is added to the pixel decoder to enhance multi-scale feature fusion. …”
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A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation by Aarthy K., Alice Nithya

“…This model enhances accuracy by incorporating self-attention mechanisms, allowing the system to focus on significant features within the data. Performance optimization is achieved through the Enhanced Chicken Swarm Optimization (ECSO) method, which fine-tunes the parameters of the system to ensure optimal results. …”
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AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. …”
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Early Sweet Potato Plant Detection Method Based on YOLOv8s (ESPPD-YOLO): A Model for Early Sweet Potato Plant Detection in a Complex Field Environment by Kang Xu, Wenbin Sun, Dongquan Chen, Yiren Qing, Jiejie Xing, Ranbing Yang

“…First, this method uses an efficient network model to enhance the information flow in the channel, obtain more effective global features in the high-level semantic structure, and reduce model parameters and computational complexity. …”
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