Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing by Xiaofei Ma, Nannan Shen, Yanhui He, Zhuo Fang, Hongyan Zhang, Yun Wang, Jinrong Duan

“…In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. …”
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A Lightweight Transformer Model for Defect Detection in Electroluminescence Images of Photovoltaic Cells by Yang Yang, Jing Zhang, Xin Shu, Lei Pan, Ming Zhang

“…Visual-based deep learning detection methods, such as Transformer and Convolutional Neural Network (CNN) models, provide a cost-effective and adaptable solution. …”
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MGL-YOLO: A Lightweight Barcode Target Detection Algorithm by Yuanhao Qu, Fengshou Zhang

“…To address this issue, this paper proposes MGL-YOLO, a lightweight one-dimensional barcode detection network based on an improved YOLOv8, which aims to achieve a high detection accuracy at low computational cost. First, a new multi-scale group convolution (MSGConv) is designed and integrated into the C2f module to construct the MSG-C2f feature extraction module. …”
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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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MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism by Yongzong Lu, Pengfei Liu, Chong Tan

“…The SDConv module reduces computational costs through depthwise separable convolution and dynamic group convolution while enhancing local feature extraction. …”
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Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion by Zhangzhang He, Xinyue Chen, Ying Gao, Yu Zhang, Yuheng Guo, Tong Zhai, Xiaochen Wei, Huan Li, Haipeng Zhu, Yongkun Fu, Zhiliang Zhang, Zhiliang Zhang

“…First, we propose a lightweight backbone network, PartialNeXt, which optimizes convolution layers through Partial Convolution (PConv), ensuring both network performance and reduced complexity. …”
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PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5. by Ming Chen, Yuqing Liu, Xing Wei, Zichen Zhang, Oleg Gaidai, Hengshou Sui, Bin Li

“…Replacing conventional convolution with dynamic convolution enhances the detection accuracy of the model and reduces the inference time. …”
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A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…To improve the recognition accuracy, the traditional classic convolutional neural network (CNN) models require higher model complexity. …”
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A Lightweight Person Detector for Surveillance Footage Based on YOLOv8n by Qicheng Wang, Guoqiang Feng, Zongzhe Li

“…Next, a heterogeneous PAFPN with improved MSBlock was formed using heterogeneous convolution kernels. Finally, AKConv, a variable kernel convolution, was applied to further reduce the number of parameters and the computational cost while improving accuracy. …”
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Twofold dynamic attention guided deep network and noise-aware mechanism for image denoising by Zihao Chen, Alex Noel Joseph Raj, Vijayarajan Rajangam, Wei Li, Vijayalakshmi G.V. Mahesh, Zhemin Zhuang

“…Convolutional neural networks are given extensive attention towards noise removal due to their good performance over traditional denoising algorithms. …”
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Enhancing Speaker Recognition with CRET Model: a fusion of CONV2D, RESNET and ECAPA-TDNN by Pinyan Li, Lap Man Hoi, Yapeng Wang, Xu Yang, Sio Kei Im

“…Although the Emphasized Channel Attention, Propagation, and Aggregation in Time Delay Neural Network (ECAPA-TDNN) model can obtain temporal context information through dilated convolution to some extent, this model falls short in acquiring fully comprehensive speech features. …”
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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

“…Second, to further reduce model complexity, a novel C2fDualHet module is proposed by integrating two consecutive Heterogeneous Kernel Convolution (HetConv) layers as a replacement for the traditional Bottleneck structure. …”
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A novel lightweight 3D CNN for accurate deformation time series retrieval in MT-InSAR by Mahmoud Abdallah, Xiaoli Ding, Samaa Younis, Songbo Wu

“…We also developed a separable convolution operator to reduce the computational costs without compromising performance. …”
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A Hybrid Content-Aware Network for Single Image Deraining by Guoqiang Chai, Rui Yang, Jin Ge, Yulei Chen

“…In CAMM, the attention mechanism is used for intricate windows to generate abundant features and simple convolution is used for plain windows to reduce computational costs. …”
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LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving by Yunchuan Yang, Shubin Yang, Qiqing Chan

“…The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. …”
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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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SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments by Quan Wang, Ye Hua, Qiongdan Lou, Xi Kan

“…The model integrates switchable atrous convolution (SAConv) and wavelet transform convolution (WTConv) for the dynamic adjustment of receptive fields for occlusion-adaptive feature extraction and to decompose features into multi-frequency sub-bands, respectively, thus preserving critical edge details of obscured targets. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…This module incorporates non-local operations under graph convolution domain to deeply explore high-order relationships between adjacent layers, while utilizing depth-wise separable convolution blocks to significantly reduce computational cost. …”
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3D-SCUMamba: An Abdominal Tumor Segmentation Model by Juwita, Ghulam Mubashar Hassan, Amitava Datta

“…Recent advancements observe the emergence of the convolution-transformer architecture, which improves segmentation performance on the cost of substantial computational resources. …”
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Nondestructive freshness recognition of chicken breast meat based on deep learning by Rui Jian, Guangbo Li, Xie Jun, Guolong Shi

“…Traditional methods for chicken breast freshness recognition suffer from issues such as high cost, difficulty in recognition, and low efficiency. …”
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