A Method for Recognizing Dead Sea Bass Based on Improved YOLOv8n by Lizhen Zhang, Chong Xu, Sai Jiang, Mengxiang Zhu, Di Wu

“…Second, the C2f-faster–EMA (efficient multi-scale attention) convolutional module was designed to replace the C2f module in the backbone network of YOLOv8n, reducing redundant calculations and memory access, thereby more effectively extracting spatial features. Then, a weighted bidirectional feature pyramid network (BiFPN) was introduced to achieve a more thorough integration of deep and shallow features. …”
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OE-YOLO: An EfficientNet-Based YOLO Network for Rice Panicle Detection by Hongqing Wu, Maoxue Guan, Jiannan Chen, Yue Pan, Jiayu Zheng, Zichen Jin, Hai Li, Suiyan Tan

“…Second, the backbone network is redesigned with EfficientNetV2, leveraging its compound scaling strategy to balance multi-scale feature extraction and computational efficiency. Third, a C3k2_DConv module improved by dynamic convolution is introduced, enabling input-adaptive kernel fusion to amplify discriminative features while suppressing background interference. …”
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Low-light image enhancement method for underground mines based on an improved Zero-DCE model by WANG Yiwei, LI Xiaoyu, WENG Zhi, BAI Fengshan

“…A Cascaded Convolution Kernel (CCK) was employed in the deep network to reduce the number of model parameters and computational cost, thereby shortening the training time. …”
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VMMT-Net: A Dual-Branch Parallel Network Combining Visual State Space Model and Mix Transformer for Land–Sea Segmentation of Remote Sensing Images by Jiawei Wu, Zijian Liu, Zhipeng Zhu, Chunhui Song, Xinghui Wu, Haihua Xing

“…The model maintains reasonable computational complexity, with only 28.24 M parameters and 25.21 GFLOPs, striking a favorable balance between accuracy and efficiency. …”
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Efficient Urban Tree Species Classification via Multirepresentation Fusion of Mobile Laser Scanning Data by Yinchi Ma, Peng Luan, Yujie Zhang, Bo Liu, Lijie Zhang

“…Quantitative assessment yielded 98.57% F1-score and 98.77% overall accuracy with moderate computational resources (2.25M parameters, 1.11G FLOPs), demonstrating significant improvements over existing methods. …”
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Design of a Drivable Area Segmentation Network Using a Field Programmable Gate Array Based on Light Detection and Ranging by Xue-Qian Lin, Jyun-Yu Jhang, Cheng-Jian Lin, Sheng-Fu Liang

“…The proposed DASNet utilizes depthwise separable convolution as a basis/platform for feature extraction to enable features to be efficiently extracted to reduce both the computational load and required network parameters. …”
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Real time weed identification with enhanced mobilevit model for mobile devices by Xiaoyan Liu, Qingru Sui, Zhihui Chen

“…Following this, we introduce an optimized MobileViT model that incorporates the Efficient Channel Attention (ECA) module into the weed feature extraction network. This design ensures robust feature extraction capabilities while simultaneously reducing the model’s parameters and computational complexity. …”
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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification. by Hong-Jie Liang, Ling-Long Li, Guang-Zhong Cao

“…Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. …”
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Lightweight Detection Methods for Multi-Scale Targets in Complex Scenarios by Anjun Yu, Zhichao Rao, Yonghua Xiong, Jinhua She

“…In this paper, we propose LAP-YOLO (Lightweight Aggregate Perception based on “You Only Look Once”), which integrates a Feature-Aware Aggregation (FAA) module to enhance feature representation and a Lightweight Information Diffusion (LID) detection head to improve small-object detection efficiency with minimal computational overhead. …”
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First-principles thermodynamic modeling for the Al-Nb-Ni ternary system by Arkapol Saengdeejing, Ryoji Sahara, Yoshiaki Toda

“…Without relying on any experimental data for solid-state phases, the first-principles Al-Nb-Ni thermodynamic database can exhibits most of the features comparing with the experimental phase diagram.…”
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Potato late blight leaf detection in complex environments by Jingtao Li, Jiawei Wu, Rui Liu, Guofeng Shu, Xia Liu, Kun Zhu, Changyi Wang, Tong Zhu

“…First, ShuffleNetV2 is used as the backbone network to reduce the number of parameters and computational load, making the model more lightweight. …”
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Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study by Marina Galanina, Anna Rekiel, Anna BaCzyk, Bozena Kostek

“…The research examines four datasets, namely DAIC-WOZ, EATD Corpus, D-Vlog, and EMU, which vary in terms of linguistic background (English and Chinese), depression classification scales, and gender representation proportions. Feature extraction employs parameters such as formant-related, MFCCs (Mel Frequency Cepstral Coefficients), and jitter parameters. …”
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YOLO-SAATD: An efficient SAR airport and aircraft target detector by Daobin Ma, Zhanhong Lu, Zixuan Dai, Yangyue Wei, Li Yang, Haimiao Hu, Wenqiao Zhang, Dongping Zhang

“…Efficiency: A lightweight hierarchical multi-scale backbone reduces parameters and enhances detection speed. 2. Fine granularity: A ”ScaleNimble Neck” integrates feature reshaping and scale-aware aggregation to enhance detail detection and feature capture in multi-scale SAR images. 3. …”
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Cotton Weed-YOLO: A Lightweight and Highly Accurate Cotton Weed Identification Model for Precision Agriculture by Jinghuan Hu, He Gong, Shijun Li, Ye Mu, Ying Guo, Yu Sun, Tianli Hu, Yu Bao

“…CW-YOLO is based on YOLOv8 and introduces a dual-branch structure combining a Vision Transformer and a Convolutional Neural Network to address the problems of the small receptive field of the CNN and the high computational complexity of the transformer. The Receptive Field Enhancement (RFE) module is proposed to enable the feature pyramid network to adapt to the feature information of different receptive fields. …”
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Optimized YOLOv8 framework for intelligent rockfall detection on mountain roads by Peng Peng, Langchao Gao, Jiachun Li, Hongzhen Zhang

“…The algorithm enhances detection performance through the following optimizations: (1) integrating a lightweight DeepLabv3+ road segmentation module at the input stage to generate mask images, which effectively exclude non-road regions from interference; (2) replacing Conv convolution units in the backbone network with Ghost convolution units, significantly reducing model parameters and computational cost while improving inference speed; (3) introducing the CPCA (Channel Priori Convolution Attention) mechanism to strengthen the feature extraction capability for targets with diverse shapes; and (4) incorporating skip connections and weighted fusion in the Neck feature extraction network to enhance multi-scale object detection. …”
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ILViT: An Inception-Linear Attention-Based Lightweight Vision Transformer for Microscopic Cell Classification by Zhangda Liu, Panpan Wu, Ziping Zhao, Hengyong Yu

“…However, existing methods still struggle with the complexity and morphological diversity of cellular images, leading to limited accuracy or high computational costs. To overcome these constraints, we propose an efficient classification method that balances strong feature representation with a lightweight design. …”
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CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer by Najamuddin NAJAMUDDIN, Usman Ullah SHEIKH, Ahmad Zuri SHA’AMERI

“…The results, evaluated on standard DeepShip and ShipsEar datasets, show that the proposed model achieved a classification accuracy of 97.98 % and 99.19 % while utilizing just 1.90 million parameters, outperforming other models such as ResNet18 and UATR-Transformer in terms of both accuracy and computational efficiency. …”
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Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging by Chun Wang, Zejun Wang, Lijiao Chen, Weihao Liu, Xinghua Wang, Zhiyong Cao, Jinyan Zhao, Man Zou, Hongxu Li, Wenxia Yuan, Baijuan Wang

“…First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. …”
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Red Raspberry Maturity Detection Based on Multi-Module Optimized YOLOv11n and Its Application in Field and Greenhouse Environments by Rongxiang Luo, Xue Ding, Jinliang Wang

“…Secondly, dilation-wise residual (DWR) is fused with the C3k2 module of the network and applied to the entire network structure to enhance feature extraction, multi-scale perception, and computational efficiency in red raspberry detection. …”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…Channel pruning further reduces the decoder’s parameters, decreasing model size and computational requirements while maintaining precision. …”
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