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CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for small object detection in Remote Sensing

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3209-3231页

作者： Weiya Shi Shaowen Zhang Shiqiang Zhang College of Artificial Intelligence and Big Data Henan University of TechnologyZhengzhou450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural *** these efforts,the detection of small objects in remote sensing remains a formidable *** deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep ***,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection ***,the sensitivity of small objects to the bounding box perturbation further increases the detection *** this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote *** address feature loss in deep layers,we have devised a cross-layer attention fusion *** noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid ***,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)*** efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available *** experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

关键词： small object detection attention mechanism cross-layer fusion discrete cosine transform

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MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for small object detection in UAV-View

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Computers, Materials & Continua 2024年第4期79卷 983-1003页

作者： Xiangyan Tang Chengchun Ruan Xiulai Li Binbin Li Cebin Fu School of Computer Science and Technology Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China School of Cyberspace Security(School of Cryptology) Hainan UniversityHaikou570228China

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion *** enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial *** build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small *** replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small ***,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple *** module enhances the perception of spatial contextual features and the utilizationof multiscale feature *** the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

关键词： small object detection YOLOv7 multi-scale attention spatial context

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Two-Layer Attention Feature Pyramid Network for small object detection

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 713-731页

作者： Sheng Xiang Junhao Ma Qunli Shang Xianbao Wang Defu Chen College of Information Engineering Zhejiang University of TechnologyHangzhou310023China Binjiang Cyberspace Security Institute of ZJUT Hangzhou310056China

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian ***,small objects are difficult to detect accurately because they contain less *** current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature ***,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object *** address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the small object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different *** the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection *** extensive experiments on challenging datasets such as Microsoft Common objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual object Classes(PASCAL VOC)demonstrate the validity of the *** results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

关键词： small object detection two-layer attention module small object detail enhancement module feature pyramid network

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Interactive Transformer for small object detection

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Computers, Materials & Continua 2023年第11期77卷 1699-1717页

作者： Jian Wei Qinzhao Wang Zixu Zhao Department of Weaponry and Control Army Academy of Armored ForcesBeijing100071China

The detection of large-scale objects has achieved high accuracy,but due to the low peak signal to noise ratio(PSNR),fewer distinguishing features,and ease of being occluded by the surroundings,the detection of small objects,however,does not enjoy similar *** to solve the problem,this paper proposes an attention mechanism based on cross-Key *** on the traditional transformer,this paper first improves the feature processing with the convolution module,effectively maintaining the local semantic context in the middle layer,and significantly reducing the number of parameters of the ***,to enhance the effectiveness of the attention mask,two Key values are calculated simultaneously along Query and Value by using the method of dual-branch parallel processing,which is used to strengthen the attention acquisition mode and improve the coupling of key ***,focusing on the feature maps of different channels,the multi-head attention mechanism is applied to the channel attention mask to improve the feature utilization effect of the middle *** comparing three small object datasets,the plug-and-play interactive transformer(IT-transformer)module designed by us effectively improves the detection results of the baseline.

关键词： small object detection attention transformer plug-and-play

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DSAFF-Net:A Backbone Network Based on Mask R-CNN for small object detection

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Computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao Dengyong Zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： small object detection classification RPN MS COCO COVID-19

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Density Map Guided Region Localization for End-to-End small object detection

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Journal of Systems Science and Information 2023年第6期11卷 776-794页

作者： Bo LI Kai HUANG Junhui LI Yufu LIAO Sichuan Jiuzhou Aerocont Technologies Co. Ltd.Mianyang 621000China

With the advancement of society and science and technology, the demand for detecting small objects in practical scenarios becomes stronger. Such objects are only represented by relatively small coverage of pixels, and the features are degraded severely after being extracted by a deep convolutional neural network, which is detrimental to the detection performance for small objects. Therefore, an intuitive solution is to increase the resolution of small objects by cropping the original image. In this paper, we propose a simple but effective object density map guided region localization module (DMGRL) to locate and crop the regions of interest where small objects may exist. Firstly, the density map of the objects is estimated by object density map estimation network, and then the coordinates of the small object regions are calculated;Secondly, the continuous differentiable affine transformation is utilized to crop these regions so that the detector with DMGRL can be trained end-to-end instead of two-stage training. Finally, the all prediction results of input image and cropped region images are merged together to output the final detection results by non maximum suppression (NMS). Extensive experiments demonstrate the superior performance of the detector incorporated DMGRL.

关键词： small object detection density map estimation end-to-end training affine transformation

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small object detection via Precise Region-Based Fully Convolutional Networks

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Computers, Materials & Continua 2021年第11期69卷 1503-1517页

作者： Dengyong Zhang Jiawei Hu Feng Li Xiangling Ding Arun Kumar Sangaiah Victor SSheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

In the past several years,remarkable achievements have been made in the field of object *** performance is generally improving,the accuracy of small object detection remains low compared with that of large object *** addition,localization misalignment issues are common for small objects,as seen in GoogLeNets and residual networks(ResNets).To address this problem,we propose an improved region-based fully convolutional network(R-FCN).The presented technique improves detection accuracy and eliminates localization misalignment by replacing positionsensitive region of interest(PS-RoI)pooling with position-sensitive precise region of interest(PS-Pr-RoI)pooling,which avoids coordinate quantization and directly calculates two-order integrals for position-sensitive score maps,thus preventing a loss of spatial precision.A validation experiment was conducted in which the Microsoft common objects in context(MS COCO)training dataset was *** showed an accuracy improvement of 3.7%for object detection tasks and an increase of 6.0%for small objects.

关键词： small object detection precise R-FCN PS-Pr-RoI pooling two-stage detector

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small object detection in High-Resolution Images Based on Multiscale detection and Re-training

Small Object Detection in High-Resolution Images Based on Mu...

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作者： Guang-min SUN Jia-yang CHEN Bing LI Dong YAN Yu LI Gang XIE Faculty of information Technology Beijing University of Technology China National Tobacco Corp.Beijing Corp

Most of the current small object detection algorithms are designed for low-resolution images. They can neither directly process high-resolution images nor make full use of the information contained. In this paper, an algorithm is proposed to detect small objects in highresolution images directly. The process of the algorithm is as follows: Firstly, an original detection task is split logically into several relevant detection sub-tasks at different scales. Secondly, a corresponding low-resolution object detector is trained for each sub-task. Thirdly, the detectors are deployed to get detection results at different scales. Finally, the multi-scale detection results are logically combined to derive the final detection results of the small objects. Besides, the detector is re-trained at small scale by introducing negative samples. The algorithm proposed in this paper was tested in the task of defect detection on building wall surface. Experimental results show the reliability and efficiency of our approach.

关键词： High resolution image small object detection Multiscale Negative feedback mechanism Defect detection of building wall surface

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Contextual Information Fusion for small object detection

Contextual Information Fusion for Small Object Detection

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第40届中国控制会议

作者： Jun Chen Xiaoqiang Chen Linbo Luo Ganbei Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on contextual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network *** experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow contextual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images.

关键词： remote sensing deep learning small object detection SSD feature fusion

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Enhancement and Fusion of Multi-Scale Feature Maps for small object detection

Enhancement and Fusion of Multi-Scale Feature Maps for Small...

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第三十九届中国控制会议

作者： Zhijun Xue Wenjie Chen Jing Li School of Automation Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center

In recent years, deep convolutional neural networks have made breakthrough progress in object recognition and object detection tasks in the field of computer vision, and have achieved great results both in accuracy and speed. However, the detection of small objects is still difficult in the field of object detection, and the accuracy on the common dataset MS COCO is very low. This paper briefly reviews some work in multi-scale object detection algorithms, and then proposes a method of feature enhancement and fusion based on multi-scale feature maps, improving detection accuracy of small objects on MS COCO.

关键词： Multi-scale Feature Enhancement and Fusion small object detection

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