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Small Object Detection with Multiscale Features [PDF]
International Journal of Digital Multimedia Broadcasting, 2018 The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image.
Guo X. Hu +4 more
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MDFE-Net: a multiscale dilated feature enhancement network for small object detection [PDF]
Frontiers in Plant ScienceDue to the lack of feature information and complex background, the task of small object detection is very challenging. To solve these problems, this paper proposes two small object detection performance enhancement modules for multiple detection tasks ...
Tianzhe Liu +4 more
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EABI-DETR: An Efficient Aerial Small Object Detection Network [PDF]
BiomimeticsSmall object detection, as an important research topic in computer vision, has been widely applied in aerial visual tasks such as remote sensing and UAV imagery.
Fufang Li, Yuehua Zhang, Yuxuan Fan
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Survey of One-Stage Small Object Detection Methods in Deep Learning [PDF]
Jisuanji kexue yu tansuo, 2022 With the development of deep learning, object detection technology has gradually changed from traditional manual detection methods to deep neural network detection methods.
LI Kecen, WANG Xiaoqiang, LIN Hao, LI Leixiao, YANG Yanyan, MENG Chuang, GAO Jing
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Small Object Detection Based on Deep Convolutional Neural Networks:A Review [PDF]
Jisuanji kexue, 2022 Small object detection has long been one of the most challenging problems in computer vision.Since small objects have the characteristics of small coverage area,low resolution,and lack of feature information,their detection effect is not ideal compared ...
DU Zi-wei, ZHOU Heng, LI Cheng-yang, LI Zhong-bo, XIE Yong-qiang, DONG Yu-chen, QI Jin
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Transformer Object Detection Algorithm Based on Multi-granularity [PDF]
Jisuanji kexue, 2023 Different from other scale objects,small objects have the characteristics of carrying less semantic information and a small number of training samples.Therefore,the current object detection algorithm has the problem of low detection accuracy for small ...
XU Fang, MIAO Duoqian, ZHANG Hongyun
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Improving small objects detection using transformer [PDF]
Journal of Visual Communication and Image Representation, 2021 General artificial intelligence is a trade-off between the inductive bias of an algorithm and its out-of-distribution generalization performance. The conspicuous impact of inductive bias is an unceasing trend of improved predictions in various problems in computer vision like object detection.
Shikha Dubey +3 more
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Small Object Detection in 3D Urban Scenes [PDF]
Jisuanji kexue, 2022 3D object detection is the core of semantic analysis in 3D urban scenes,but the existing object detection methods mainly focus on large objects such as buildings and roads,while the detection accuracy of these methods for small objects such as street ...
CHEN Jia-zhou, ZHAO Yi-bo, XU Yang-hui, MA Ji, JIN Ling-feng, QIN Xu-jia
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Lightweight multi-scale network for small object detection [PDF]
PeerJ Computer Science, 2022 Small object detection is widely used in the real world. Detecting small objects in complex scenes is extremely difficult as they appear with low resolution.
Li Li, Bingxue Li, Hongjuan Zhou
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Small Object Detection Based on Improved YOLOv7 [PDF]
Jisuanji gongcheng, 2023 Despite advancements in object detection technology, Small Object Detection(SOD) is still difficult to research.To address the challenge of easily missing detection in the process of object detection, this study proposes an improved YOLOv7 object ...
QI Linglong, GAO Jianling
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