CSW-YOLO: A traffic sign small target detection algorithm based on YOLOv8. by Qian Shen, Yi Li, YuXiang Zhang, Lei Zhang, ShiHao Liu, Jinhua Wu

“…First, the bottleneck of the C2f module in the original yolov8 network is replaced with the residual Faster-Block module in FasterNet, and then the new channel mixer convolution GLU (CGLU) in TransNeXt is combined with it to construct the C2f-faster-CGLU module, reducing the number of model parameters and computational load; Secondly, the SPPF module is combined with the large separable kernel attention (LSKA) to construct the SPPF-LSKA module, which greatly enhances the feature extraction ability of the model; Then, by adding a small target detection layer, the accuracy of small target detection such as traffic signs is greatly improved; Finally, the Inner-IoU and MPDIoU loss functions are integrated to construct WISE-Inner-MPDIoU, which replaces the original CIoU loss function, thereby improving the calculation accuracy. …”
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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. …”
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Multichannel Aligned Feature Fusion Method for Salient Object Detection in Optical Remote Sensing Images by Weining Zhai, Liejun Wang, Panpan Zheng, Lele Li

“…Salient Object Detection (SOD), an important preprocessing part of image processing, identifies and labels the most attention-grabbing objects by simulating human vision. …”
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Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan, Zhiqi Gao

“…Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. Firstly, the backbone network integrates depthwise separable convolutions and a Convolutional Block Attention Module (CBAM) to suppress background clutter and extract effective features. …”
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High resolution weld semantic defect detection algorithm based on integrated double U structure by Xiaoyan Li, Yi Wei, Zhigang Lv, Peng Wang, Liangliang Li, Mengyu Sun, Chu Wang

“…In terms of model architecture, by improving U2Netp and UNet networks, MC-SPP module (multi-connection spatial pyramid pooling), RMAG module (residual multi-add gating recurrent unit), HDC-CBAM module (hybrid dilated convolution-convolutional block attention) and CCM module (cross-layer connection fusion) were integrated to form a cascade network with multi-level feature fusion capability. …”
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A Deep-Learning-Based Detection Method for Small Target Tomato Pests in Insect Traps by Song Wang, Daqing Chen, Jianxia Xiang, Cong Zhang

“…Thirdly, the feature fusion part takes the P2 feature layer into account and adds a P2 small target detection head. …”
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<i>DSW-YOLO</i>-Based Green Pepper Detection Method Under Complex Environments by Yukuan Han, Gaifeng Ren, Jiarui Zhang, Yuxin Du, Guoqiang Bao, Lijun Cheng, Hongwen Yan

“…A <i>SimAM</i> parameter-free attention mechanism was added to the last layer of the backbone, boosting P, R, mAP50, and mAP50-95 to 90.6%, 84.0%, 91.8%, and 68.5%, and reducing average detection time to 1.1 ms. …”
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Interpretable multi-horizon time series forecasting of cryptocurrencies by leverage temporal fusion transformer by Arslan Farooq, M. Irfan Uddin, Muhammad Adnan, Ala Abdulsalam Alarood, Eesa Alsolami, Safa Habibullah

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Early detection of Alzheimer’s disease progression stages using hybrid of CNN and transformer encoder models by Hassan Almalki, Alaa O. Khadidos, Nawaf Alhebaishi, Ebrahim Mohammed Senan

“…Finally, the MLP classification layers classify each image into one of four dataset classes. …”
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Edge-Guided Feature Pyramid Networks: An Edge-Guided Model for Enhanced Small Target Detection by Zimeng Liang, Hua Shen

“…Infrared small target detection technology has been widely applied in the defense sector, including applications such as precision targeting, alert systems, and naval monitoring. …”
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A Hybrid Convolutional–Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson’s Disease Detection by Chayut Bunterngchit, Laith H. Baniata, Hayder Albayati, Mohammad H. Baniata, Khalid Alharbi, Fanar Hamad Alshammari, Sangwoo Kang

“…To overcome these challenges, this study proposes a convolutional transformer enhanced sequential model (CTESM), which integrates convolutional neural networks, transformer attention blocks, and long short-term memory layers to capture spatial, temporal, and sequential EEG features. …”
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LGR-Net: A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates by Ruizhen Gao, Meng Chen, Yue Pan, Jiaxin Zhang, Haipeng Zhang, Ziyue Zhao

“…To improve the localization accuracy for small defects, we add a high-resolution small object detection layer (P2 layer) and integrate the Convolutional Block Attention Module (CBAM) to construct a four-scale feature fusion network. …”
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Campus risk detection using the S-YOLOv10-SIC network and a self-calibrated illumination algorithm by Qiang Zhao, Sha Liu, Shihao Zhang, Baijuan Wang

“…StarNet is employed to enhance the original network structure, feature extraction capability, and decrease parameter count and calculations. The Convolutional Block Attention Module is incorporated into the small-object layer to boost network attention, subdue background noise, and enhance recognition accuracy and generalization capability. …”
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Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n by Lili Song, Haixin Deng, Jianfeng Han, Xiongwei Gao

“…The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model’s capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model’s convergence rate and regression accuracy. …”
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Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast by Yuan Qi, Tan Liu, Songlin Guo, Peiyan Wu, Jun Ma, Qingyun Yuan, Weixiang Yao, Tongyu Xu

“…The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. …”
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SSCD-YOLO: Semi-Supervised Cross-Domain YOLOv8 for Pedestrian Detection in Low-Light Conditions by Fangliang Cao, Kai Yan, Hongliang Chen, Zhen Wang, Yunliang Du, Zekang Zheng, Kefan Li, Baozhu Qi, Mingjia Wang

“…First, a CDF-CycleGAN cross-domain image fusion method is designed, which improves the input of CycleGAN using a diffusion model, integrates the self-attention mechanism into the residual network of the CycleGAN generator to design the SA-Block, replaces the linear layer in the CycleGAN discriminator with an autoencoder, and improves the training method of CycleGAN. …”
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Accurate and lightweight oral cancer detection using SE-MobileViT on clinically validated image dataset by Md Firoz Kabir, Md Yousuf Ahmad, Roise Uddin, Martin Cordero, Shashi Kant

“…To further enrich feature representation and spatial attention, a Squeeze-and-Excitation block is embedded after the third convolutional layer. …”
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MXT-YOLOv7t: An Efficient Real-Time Object Detection for Autonomous Driving in Mixed Traffic Environments by Afdhal Afdhal, Khairun Saddami, Mirshal Arief, Sugiarto Sugiarto, Zahrul Fuadi, Nasaruddin Nasaruddin

“…This model enhances YOLOv7-tiny P5 by improving the detection rate and reducing inference time. The enhancements include refining the feature extraction network by integrating a lightweight attention mechanism into the ELAN blocks and replacing the activation function in each convolution layer with a sigmoid-weighted linear unit. …”
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Gradient tri-layered TGOs in MoSi2/HfO2 duplex EBCs for effective protection of SiC substrate against steam corrosion at 1500 °C for 200 h by Kexue Peng, Ying Qiao, Qian Li, Xinxin Cao, Long Wang, Guifang Han, Jianzhang Li, Jingyu Qin, Jingde Zhang

“…The incorporation of gradient Hf doping and HfO2/HfSiO4 particle reinforcement effectively suppressed the crystallization and phase transition of SiO2 and mitigated the internal stress within the EBCs, generating a crack-blocking effect. This effect prevented the scale of the TGOs from further channel crack propagation, enabling the SiC substrate with no detectable corrosion after 200 h of exposure at 1500 °C in steam, even when the TGOs thickness reached 24.5 μm. …”
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AJANet: SAR Ship Detection Network Based on Adaptive Channel Attention and Large Separable Kernel Adaptation by Yishuang Chen, Jie Chen, Long Sun, Bocai Wu, Hui Xu

“…In addition, a large kernel attention block with adaptive kernel size is introduced to automatically adjust the receptive field designed to extract abundant context information at different detection layers. …”
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