Lightweight detection and segmentation of crayfish parts using an improved YOLOv11n segmentation model by Wei Shi, Jun Zhang, YunFan Fu, DanWei Chen, JianPing Zhu, ChunFeng Lv

“…First, the proposed D-HGNetV2 backbone integrates DynamicConv modules into the HGNetV2 architecture, reducing parameters by 31% (from 2.9 M to 2.0 M) and computational cost by 9.8% (10.2 GFLOPs to 9.2 GFLOPs) through input-dependent kernel aggregation, which enhances multiscale feature extraction for occluded or overlapping parts. …”
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Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures by Yu Qiao, Marius Grad, Aida Nonn

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FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

“…Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. …”
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Preliminary Design of Debris Removal Missions by Means of Simplified Models for Low-Thrust, Many-Revolution Transfers by Federico Zuiani, Massimiliano Vasile

“…This paper presents a novel approach for the preliminary design of Low-Thrust, many-revolution transfers. The main feature of the novel approach is a considerable reduction in the control parameters and a consequent gain in computational speed. …”
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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection by Zrar Kh Abdul, Abdulbasit K. Al-Talabani, Wisam Hazim Gwad, Entisar Alkayal, Halgurd S. Maghdid, Safar Maghdid Asaad

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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…Additionally, inverted residual connections are incorporated to expand the model’s receptive field. To maximize feature utilization, cross-layer connections are introduced between the two parallel streams, integrating the Efficient Channel Attention (ECA) module to reduce the number of parameters. …”
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A Lightweight Neural Network for Cell Segmentation Based on Attention Enhancement by Shuang Xia, Qian Sun, Yiheng Zhou, Zhaoyuxuan Wang, Chaoxing You, Kainan Ma, Ming Liu

“…Deep neural networks have made significant strides in medical image segmentation tasks, but their large-scale parameters and high computational complexity limit their applicability on resource-constrained edge devices. …”
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Ghost Module-Enhanced MTCNN: A Lightweight Cascade Framework for High-Accuracy Face Detection in Edge-Deployable Scenarios by Chen Wang, Fen Liu

“…This modification reconstructs the network’s feature extraction capabilities, resulting in a new model. …”
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An Improved YOLOv8n-Based Method for Detecting Rice Shelling Rate and Brown Rice Breakage Rate by Zhaoyun Wu, Yehao Zhang, Zhongwei Zhang, Fasheng Shen, Li Li, Xuewu He, Hongyu Zhong, Yufei Zhou

“…This paper proposes a high-precision, lightweight solution based on an enhanced YOLOv8n with improvements in network architecture, feature fusion, and attention mechanism. The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. …”
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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. …”
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Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning by Maëliss Jallais, Marco Palombo

“…Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. …”
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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. …”
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EFCNet for small object detection in remote sensing images by Yutong Wang, Zhensong Li, Shiliang Zhu, Xiaotan Wei

“…Experiments conducted on the DOTA and DIOR datasets demonstrate that our model achieves an average precision mean of 75.9% and 80.5%, respectively, with the model’s parameters and computational requirements amounting to 13.4M and 30.2 GFLOPs, respectively. …”
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A Lightweight Direction-Aware Network for Vehicle Detection by Luxia Yang, Yilin Hou, Hongrui Zhang, Chuanghui Zhang

“…Moreover, to further reduce model parameters and computational requirements, a lightweight shared convolutional detection head (SCL-Head) is devised using a parameter-sharing mechanism. …”
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Sparse intensity sampling for ultrafast full-field reconstruction in low-dimensional photonic systems by Egor Manuylovich

“…Abstract Phase-sensitive measurements usually utilize interferometric techniques to retrieve the optical phase. However, when the feature space of an electromagnetic field is inherently low dimensional, most field parameters can be extracted from intensity measurements only. …”
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Adaptive Aberration Correction for Laser Processes Improvement by Carmelo Corsaro, Priscilla Pelleriti, Vincenza Crupi, Daniele Cosio, Fortunato Neri, Enza Fazio

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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. …”
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Dense skip-attention for convolutional networks by Wenjie Liu, Guoqing Wu, Han Wang, Fuji Ren

“…Notably, it achieves these improvements without significantly increasing model parameters or computational cost, maintaining minimal impact on both aspects.…”
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A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance by Ye Mu, Jinghuan Hu, Zhipeng Li, Heyang Wang, He Gong, Yu Sun, Tianli Hu

“…A dual-channel architecture is implemented, integrating Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) to synergize local feature extraction with global contextual modeling, thereby mitigating CNN’s limited receptive fields and ViT’s computational complexity. …”
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Instinctive Recognition of Pathogens in Rice Using Reformed Fractional Differential Segmentation and Innovative Fuzzy Logic-Based Probabilistic Neural Network by Anusha Preetham, Sayed Sayeed Ahmad, Ihab Wattar, Pooja Singh, Sandeep Rout, Mejdal A. Alqahtani, Enoch Tetteh Amoatey

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