Quantitative Ultrasound Texture Analysis of Breast Tumors: A Comparison of a Cart-Based and a Wireless Ultrasound Scanner by David Alberico, Lakshmanan Sannachi, Maria Lourdes Anzola Pena, Joyce Yip, Laurentius O. Osapoetra, Schontal Halstead, Daniel DiCenzo, Sonal Gandhi, Frances Wright, Michael Oelze, Gregory J. Czarnota

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A lightweight weed detection model for cotton fields based on an improved YOLOv8n by Jun Wang, Zhengyuan Qi, Yanlong Wang, Yanyang Liu

“…Finally, a lightweight detection head, LiteDetect, suitable for the BiFPN structure, is designed to streamline the model structure and reduce computational load. Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively.…”
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YOLOv10-kiwi: a YOLOv10-based lightweight kiwifruit detection model in trellised orchards by Jie Ren, Wendong Wang, Yuan Tian, Jinrong He

“…This replacement enables parallel processing and enhances feature extraction efficiency. By combining heterogeneous kernels in sequence, C2fDualHet captures both local and global features while significantly lowering parameter count and computational cost. …”
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A lightweight trichosanthes kirilowii maxim detection algorithm in complex mountain environments based on improved YOLOv7-tiny. by Zhongjian Xie, Xinwei Chen, Weilin Wu, Yao Xiao, Yuanhang Li, Yaya Zhang, ZhuXuan Wan, Weiqi Chen

“…However, the environmental characteristics of brightness variation, inter-plant occlusion, and motion-induced blurring during harvesting operations, detection algorithms face excessive parameters and high computational intensity. Accordingly, this study proposes a lightweight T.Kirilowii detection algorithm for complex mountainous environments based on YOLOv7-tiny, named KPD-YOLOv7-GD. …”
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Steel surface defect detection method based on improved YOLOv9 by Cong Chen, Hoileong Lee, Ming Chen

“…To improve the recognition accuracy of small targets, a bidirectional feature pyramid network (BiFPN) is integrated, enabling the model to capture small target features more precisely. …”
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A lightweight MHDI-DETR model for detecting grape leaf diseases by Zilong Fu, Lifeng Yin, Can Cui, Yi Wang

“…The original residual backbone network was improved using the MobileNetv4 network, significantly reducing the model’s computational requirements and complexity. Additionally, a lightSFPN feature fusion structure is presented, combining the Hierarchical Scale Feature Pyramid Network with the Dilated Reparam Block structure design from the UniRepLKNet network. …”
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification by Yao Lu

“…Specifically, our approach incorporates depthwise separable convolutions and Squeeze-and-Excitation (SE) attention modules to create a model that is both computationally efficient and highly effective for landscape feature extraction. …”
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A Lightweight Multi-Scale Model for Speech Emotion Recognition by Haoming Li, Daqi Zhao, Jingwen Wang, Deqiang Wang

“…A_Inception combines the merits of Inception module and attention-based rectified linear units (AReLU) and thus can learn multi-scale features adaptively with low computational cost. Meanwhile, to extract most important emotional information, we propose a new multiscale cepstral attention and temporal-cepstral attention (MCA-TCA) module. …”
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Vehicle detection in drone aerial views based on lightweight OSD-YOLOv10 by Yang Zhang, Xiaobing Chen, Su Sun, Hongfeng You, Yuanyuan Wang, Jianchu Lin, Jiacheng Wang

“…The proposed algorithm incorporates several key innovations: First, we employ online convolutional reparameterization to construct the OCRConv module and design a lightweight feature extraction structure, SPCC, to replace the conventional C2f module, thereby reducing computational load and parameter count. …”
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Lightweight obstacle detection for unmanned mining trucks in open-pit mines by Guangwei Liu, Jian Lei, Zhiqing Guo, Senlin Chai, Chonghui Ren

“…This network has the advantages of simple structure and high lightweight, which effectively reduces the amount of calculation and parameters of the model. Then the feature extraction structure of the YOLOv8 neck is replaced with the BiFPN (Bi-directional Feature Pyramid Network) structure. …”
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ConvTransNet-S: A CNN-Transformer Hybrid Disease Recognition Model for Complex Field Environments by Shangyun Jia, Guanping Wang, Hongling Li, Yan Liu, Linrong Shi, Sen Yang

“…This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. …”
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A Lightweight Transformer Edge Intelligence Model for RUL Prediction Classification by Lilu Wang, Yongqi Li, Haiyuan Liu, Taihui Liu

“…To address this issue, we propose TBiGNet, a lightweight Transformer-based classification network model for RUL prediction. TBiGNet features an encoder–decoder architecture that outperforms traditional Transformer models by achieving over 15% higher accuracy while reducing computational load, memory access, and parameter size by more than 98%. …”
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Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM by Yulin Wang, Xianjun Du

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A Heterogeneous Image Registration Model for an Apple Orchard by Dongfu Huang, Liqun Liu

“…Then, we used the Sinkhorn AutoDiff algorithm to iteratively optimize and solve the optimal transmission problem, achieving optimal matching between feature points. Finally, we carried out network pruning and compression operations to minimize parameters and computation cost while maintaining the model’s performance. …”
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Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions by Daniel A. Serino, Evan Bell, Marc Klasky, Ben S. Southworth, Balasubramanya Nadiga, Trevor Wilcox, Oleg Korobkin

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A lightweight Xray-YOLO-Mamba model for prohibited item detection in X-ray images using selective state space models by Kai Zhao, Shufan Peng, Yujin Li, Tianliang Lu

“…Despite significant advancements in deep learning, challenges such as feature extraction, object occlusion, and model complexity remain. …”
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MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection by Jianhua Ye, Pan Li, Yunda Zhang, Ze Guo, Shoujin Zeng, Youji Zhan

“…Additionally, the network’s computational GLOPS is 13.45 G, and the number of parameters is 46.92 M, which can meet the requirements for real-time detection. …”
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TFDense-GAN: a generative adversarial network for single-channel speech enhancement by Haoxiang Chen, Jinxiu Zhang, Yaogang Fu, Xintong Zhou, Ruilong Wang, Yanyan Xu, Dengfeng Ke

“…In particular, the Unet architecture, which comprises three main components, the encoder, the decoder, and the bottleneck, employs DenseBlock in both the encoder and the decoder to achieve powerful feature fusion capabilities with fewer parameters. …”
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Finite-size effects in molecular simulations: a physico-mathematical view by Benedikt M. Reible, Carsten Hartmann, Luigi Delle Site

“…Here this feature is treated employing the same statistical mechanics framework developed for the first problem.…”
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ShadowFPN-YOLO: A Real-Time NMS-Free Detector for Remote Sensing Ship Detection by Xiao Yang, Ahmad Sufril Azlan Mohamed, Chuanchuan Wang

“…Experimental results demonstrate the strong performance of our method, achieving mAP values of 55.70 at 430 FPS on the DIOR-Ship dataset and 55.85 at 497 FPS on the HRSID dataset, all while maintaining the fewest parameters and the lowest computational cost compared to the latest YOLO models. …”
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