Research on PCB defect detection algorithm based on LPCB-YOLO by Haiyan Zhang, Haiyan Zhang, Yazhou Li, Yazhou Li, Dipu Md Sharid Kayes, Dipu Md Sharid Kayes, Zhaoyu Song, Zhaoyu Song, Yuanyuan Wang, Yuanyuan Wang

“…The goal was to ensure detection accuracy and comprehensiveness while significantly reducing model parameters and improving computational speed.MethodFirst, the feature extraction networks consist of multiple CSPELAN modules for feature extraction of small target defects on PCBs. …”
Get full text

A lightweight high-frequency mamba network for image super-resolution by Tao Wu, Wei Xu, Yajuan Wu

“…It can better incorporate local and global information and has linear complexity in the global feature extraction branch. Experiments on multiple benchmark datasets demonstrate that the network outforms recent SOTA methods in SISR while using fewer parameters. …”
Get full text

YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships by Liran Shen, Tianchun Gao, Qingbo Yin

“…However, achieving accurate detection for small ships is a challenge for cost-efficiency models; while the models could meet this requirement, they have unacceptable computation costs for real-time surveillance. We propose YOLO-LPSS, a novel model designed to significantly improve small ship detection accuracy with low computation cost. …”
Get full text

TEBS: Temperature–Emissivity–Driven band selection for thermal infrared hyperspectral image classification with structured State-Space model and gated attention by Enyu Zhao, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng

“…Subsequently, a weight computation (WC) module, leveraging SSM and GAM, is developed to generate robust band weights by sequentially leveraging multi-scale LST features. …”
Get full text

DOA Estimation Based on CNNs Embedded With Mamba by Zhang Ziyan, Yi Shichao, Wang Chengyi

“…However, feature processing of the original signal inevitably loses part of the information, and using the original signal directly makes the number of parameters and computation huge. …”
Get full text

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

“…This module performs convolution in stages on the feature map, generating more feature maps with fewer parameters and computational resources, thereby improving the model’s feature extraction capability while reducing parameter count and computational cost. …”
Get full text

A Lightweight Greenhouse Tomato Fruit Identification Method Based on Improved YOLOv11n by Xingyu Gao, Fengyu Li, Jun Yan, Qinyou Sun, Xianyong Meng, Pingzeng Liu

“…Meanwhile, the model size is only 3.3 MB, the number of parameters is 1.6 M, and the floating-point computation is 3.9 GFLOPs. …”
Get full text

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. …”
Get full text

MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery by Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan

“…To address the issues of high computational complexity and boundary feature loss encountered when extracting farmland information from high-resolution remote sensing images, this study proposes an innovative CNN–Transformer hybrid network, MAMNet. …”
Get full text

GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

“…The number of parameters and computational load of the improved model have been reduced by 45.7% and 45.1%, respectively, compared to the baseline model, while the mAP has increased by 1.3%. …”
Get full text

Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow by Vinicius L.S. Silva, Pablo Salinas, Claire E. Heaney, Matthew D. Jackson, Christopher C. Pain

“…Furthermore, this work performs a sensitivity study in the dimensionless parameters (machine learning features), assess the efficacy of various machine learning models, demonstrate a decrease in nonlinear iterations using our method in more intricate, realistic three-dimensional models, and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85% reduction in computational time.…”
Get full text

GES-YOLO: A Light-Weight and Efficient Method for Conveyor Belt Deviation Detection in Mining Environments by Hongwei Wang, Ziming Kou, Yandong Wang

“…To address issues such as the high computational complexity, large number of parameters, long inference time, and difficulty in feature extraction of existing conveyor belt deviation detection models, we propose a GES-YOLO algorithm for detecting deviation in mining belt conveyors, based on an improved YOLOv8s model. …”
Get full text

FQDNet: A Fusion-Enhanced Quad-Head Network for RGB-Infrared Object Detection by Fangzhou Meng, Aoping Hong, Hongying Tang, Guanjun Tong

“…FQDNet was evaluated on three public RGB-IR datasets—M3FD, VEDAI, and LLVIP—achieving mAP@[0.5:0.95] gains of 4.4%, 3.5%, and 3.1% over the baseline, with only a 0.4 M increase in parameters and 5.5 GFLOPs overhead. Compared to state-of-the-art RGB-IR object detection algorithms, our method strikes a better balance between detection accuracy and computational efficiency while exhibiting strong robustness across diverse detection scenarios.…”
Get full text

IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…Second, we design a multi-receptive attention and interaction mechanism to perceive global and local correlations of the images in every transformer block for effective feature learning for small-sized networks. Extensive experiments show that the proposed lightweight IMViT-B outperforms DeiTIII, this paper IMViT-B(300 epochs) achieves a top accuracy of <inline-formula> <tex-math notation="LaTeX">$82.8~\%$ </tex-math></inline-formula> on ImageNet-1K with only 26M parameters, surpasses the DeiTIII-S(800 epochs) +1.4%, with a similar number of parameters and computation cost. …”
Get full text

A lightweight transformer with linear self‐attention for defect recognition by Yuwen Zhai, Xinyu Li, Liang Gao, Yiping Gao

“…LSA‐Former proposes a novel self‐attention with linear computational complexity, enabling it to capture local and global semantic features with fewer parameters. …”
Get full text

YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection by Xiang Gan, Shukun Cao, Jin Wang, Yu Wang, Xu Hou

“…Based on the original YOLOv8n, the algorithm replaced the Conv module with the DSConv module in the backbone network, which reduced the backbone network parameters and computational load and improved the detection accuracy at the same time. …”
Get full text

The TDGL Module: A Fast Multi-Scale Vision Sensor Based on a Transformation Dilated Grouped Layer by Leilei Xie, Fenghua Zhu, Zhixue Wang

“…These improvements enable the network to distinguish features at different scales effectively while optimizing spatial information processing and reducing computational costs. …”
Get full text

GCB‐YOLO: A Lightweight Algorithm for Wind Turbine Blade Defect Detection by Zhiming Zhang, Chaoyi Dong, Ze Wei, Xiaoyan Chen, Weidong Zan, Yao Xue

“…Initially, a GhostNet network was employed to replace a portion of the YOLOv5s backbone network responsible for feature extraction. This replacement serves to reduce the network's parameter size and computational load, thereby achieving compression of the feature extraction network. …”
Get full text

A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation by Qingxu Meng, Weijiang Wang, Hang Qi, Hua Dang, Minli Jia, Xiaohua Wang

“…Although the combination of CNNs and Transformers balances the ability to capture local detailed features and global context information, it inevitably increases the model’s parameters and computational cost, which restricts its equal deployment in real medical scenarios. …”
Get full text

A lightweight detection algorithm of PCB surface defects based on YOLO. by Shiwei Yu, Feng Pan, Xiaoqiang Zhang, Linhua Zhou, Liang Zhang, Jikui Wang

“…Aiming at the problems of low accuracy and large computation in the task of PCB defect detection. This paper proposes a lightweight PCB defect detection algorithm based on YOLO. …”
Get full text