Multi-Scale Feature Fusion Enhancement for Underwater Object Detection by Zhanhao Xiao, Zhenpeng Li, Huihui Li, Mengting Li, Xiaoyong Liu, Yinying Kong

“…Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. …”
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Deep blur detection network with boundary-aware multi-scale features by Xiaoli Sun, Qiwei Wang, Xiujun Zhang, Chen Xu, Weiqiang Zhang

“…To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. First, the VGG-16 network is used to extract the deep features from multi-scale layers. …”
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Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection by Yue Wang, Xinhong Wang, Shi Qiu, Xianghui Chen, Zhaoyan Liu, Chuncheng Zhou, Weiyuan Yao, Hongjia Cheng, Yu Zhang, Feihong Wang, Zhan Shu

“…Although convolutional neural networks (CNNs) increase robustness to diverse scenes with a data-driven paradigm, many CNN-based methods are insufficient in capturing fine-grained details necessary for small targets and are less effective during multi-scale feature fusion. To overcome these challenges, we propose the novel Wide-scale Gated Fully Fusion Network (WGFFNet) in this article, which contributes to infrared small-target detection (IRSTD). …”
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Multi-Scale Feature Pyramid Network With Camera Artifact Augmentation for Pedestrian Detection by Ankit Shrivastava, Shanmugam Poonkuntran

“…Nowadays, pedestrian detection is considered as an important area of applications in automated driving and surveillance systems. This paper presents a novel hybrid model termed as multi-scale feature pyramid network with camera artifact augmentation (MSFPN-CAA) that integrates Feature Pyramid Networks (FPN) with a fine-tuned YOLOv10 model for enhanced pedestrian detection. …”
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Liver segmentation network based on detail enhancement and multi-scale feature fusion by Lu Tinglan, Qin Jun, Qin Guihe, Shi Weili, Zhang Wentao

“…Furthermore, to enable the model to better learn liver features at different scales, a Multi-Scale Feature Fusion module (MSFF) is added to the skip connections in the model. …”
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Continuous Sign Language Recognition With Multi-Scale Spatial-Temporal Feature Enhancement by Zhen Wang, Dongyuan Li, Renhe Jiang, Manabu Okumura

“…A successful CSLR method relies on the continuous tracking of the presenter’s gestures and facial movements. Existing CSLR methods struggle with fully leveraging fine-grained continuous frame information and often overlook the importance of multi-scale feature integration during decoding. …”
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MSM: a scaling-based feature matching algorithm for images with large-scale differences by Qifeng Ge, Xiaoping Du, Chen Xu, Jianhao Xu, Zhenzhen Yan, Xiangtao Fan

“…In light of limitations, this study proposed a feature matching algorithm based on scaling, MSM (Multi-Scale Matching), which enhances feature matching performance in images with large-scale variations. …”
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MSDNet: A Multi-Scale Feature Representation Network Model for Tunnel Bolt Detection by Xiufeng Wu, Xueli Li, Guangxing Cao, Chao Guo, Wei Chang, Zhao Wang, Shuliang Liu

“…To address these issues, we introduces a novel multi-scale feature extraction detection network (MSDNet) designed to improve tunnel bolt maintenance by reducing false positives and missed detections. …”
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Multi‐scale feature extraction for energy‐efficient object detection in remote sensing images by Di Wu, Hongning Liu, Jiawei Xu, Fei Xie

“…In this paper, an improved detector based on RTMDet, called the Multi‐Scale Feature Extraction‐assist RTMDet (MRTMDet), is proposed which address limitations through enhancement feature extraction and fusion networks. …”
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PD-YOLO: a novel weed detection method based on multi-scale feature fusion by Shengzhou Li, Zihan Chen, Jialong Xie, Hewei Zhang, Jianwen Guo

“…However, weed detection through computer vision presents various challenges, such as morphological similarities between weeds and crops, large-scale variations, occlusions, and the small size of the target objects.MethodsTo overcome these challenges, this paper proposes a novel object detection model, PD-YOLO, based on multi-scale feature fusion. …”
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MCFA: Multi-Scale Cascade and Feature Adaptive Alignment Network for Cross-View Geo-Localization by Kaiji Hou, Qiang Tong, Na Yan, Xiulei Liu, Shoulu Hou

“…To address these issues, we propose a Multi-Scale Cascade and Feature Adaptive Alignment (MCFA) network, which consists of a Multi-Scale Cascade Module (MSCM) and a Feature Adaptive Alignment Module (FAAM). …”
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A Vehicle–Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion by Jianhu Liu, Ping Wang, Xia Wu

“…Our approach includes a Multi-Scale Dynamic Feature Fusion Module designed to comprehensively integrate features from both vehicle and infrastructure across spatial and semantic dimensions. …”
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Multi-Scale Feature Mixed Attention Network for Cloud and Snow Segmentation in Remote Sensing Images by Liling Zhao, Junyu Chen, Zichen Liao, Feng Shi

“…The framework integrates three key components: (1) a Multi-scale Pooling Feature Perception Module to capture multi-level structural features, (2) a Bilateral Feature Mixed Attention Module that enhances boundary detection through spatial-channel attention, and (3) a Multi-scale Feature Convolution Fusion Module to reduce edge blurring. …”
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YOLO-MAD: Multi-Scale Geometric Structure Feature Extraction and Fusion for Steel Surface Defect Detection by Hantao Ding, Junkai Chen, Hairong Ye, Yanbing Chen

“…The model also shows enhanced recognition performance for most defect categories. This work presents a pioneering approach that balances lightweight design with high detection performance by efficiently leveraging multi-scale geometric feature extraction and fusion, offering a new paradigm for industrial defect detection.…”
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Multi-Scale Geometric Feature Extraction and Global Transformer for Real-World Indoor Point Cloud Analysis by Yisheng Chen, Yu Xiao, Hui Wu, Chongcheng Chen, Ding Lin

“…The MSGFE module consists of a geometric feature extraction (GFE) module and a multi-scale attention (MSA) module. …”
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MSF-SLAM: Enhancing Dynamic Visual SLAM with Multi-Scale Feature Integration and Dynamic Object Filtering by Yongjia Duan, Jing Luo, Xiong Zhou

“…At the core of our approach lies a novel Multi-Scale Feature Consolidation (MSFConv) module, which we have developed to significantly boost the feature extraction capabilities of the YOLOv8 network. …”
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MDA-DETR: Enhancing Offending Animal Detection with Multi-Channel Attention and Multi-Scale Feature Aggregation by Haiyan Zhang, Huiqi Li, Guodong Sun, Feng Yang

“…This article introduces Multi-Channel Coordinated Attention and Multi-Dimension Feature Aggregation (MDA-DETR). It integrates multi-scale features for enhanced detection accuracy, employing a Multi-Channel Coordinated Attention (MCCA) mechanism to incorporate location, semantic, and long-range dependency information and a Multi-Dimension Feature Aggregation Module (DFAM) for cross-scale feature aggregation. …”
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Heterogeneous attention multi-scale network for efficient weld seam classification by Enpei Guo, Feixu Chen

“…Our key innovation lies in the synergistic integration of four components: a Channel-Spatial Attention module for precise feature focus, a Heterogeneous Attention Pooling module for multi-scale feature capture, a computationally efficient Ghost Feature Channel ReLU layer, and an Adaptive Feature Pyramid Network for dynamic feature fusion. …”
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MDEU-Net: Medical Image Segmentation Network Based on Multi-Head Multi-Scale Cross-Axis by Shengxian Yan, Yuyang Lei, Jing Zhang, Xiao Gao, Xiang Li, Penghui Wang, Hui Cao

“…To address these limitations, this paper presents a novel network architecture, multi-head multi-scale cross-axis attention MDEU-Net, that leverages a multi-head attention mechanism processing input features in parallel. …”
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An Explainable Deep Learning Framework for Sorghum Weed Classification Using Multi-Scale Feature Enhanced DenseNet by Armaano Ajay, S. Sandosh, Aryan Saji, Harsh Agarwal

“…These blocks improve multi-scale feature extraction and adaptively capture complex patterns from the weed images. …”
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