Results 21 to 30 of about 159,708 (297)
MDFE-Net: a multiscale dilated feature enhancement network for small object detection. [PDF]
Front Plant SciDue 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 ...
Liu T, Lin S, Zhang J, Li B, Zhu J.
europepmc +2 more sources
Knowledge-Assisted Small Object Detection
Small Object Detection (SOD) is a challenging task due to the small size of objects and the complexity of noisy backgrounds, which are common in fields like surveillance and autonomous driving.
Nguyen, Quoc Viet +3 more
core +2 more sources
PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection. [PDF]
Sci RepIn the domain of object detection, small object detection remains a pressing challenge, as existing approaches often suffer from limited accuracy, high model complexity, and difficulty meeting lightweight deployment requirements.
Chen W, Liu J, Liu T, Zhuang Y.
europepmc +2 more sources
Small Object Detection Based on Two-Stage Calculation Transformer [PDF]
Jisuanji kexue yu tansuo, 2023 Despite the current small object detection task has achieved significant improvements, it still suffers from some problems. For example, it is a challenge to extract small object features because of little information in the scene of small objects, which
XU Shoukun, GU Jianan, ZHUANG Lihua, LI Ning, SHI Lin, LIU Yi
doaj +2 more sources
Towards Large-Scale Small Object Detection: Survey and Benchmarks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously ...
Gong Cheng, Xiwen Yao, Kebing Yan
exaly +3 more sources
YOLO‐RSFM: An efficient road small object detection method
IET Image ProcessingTo tackle challenges in road multi‐object detection, such as object occlusion, small object detection, and multi‐scale object detection difficulties, a new YOLOv8n‐RSFM structure is proposed. The key improvement of this structure lies in the introduction
Pei Tang +4 more
doaj +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
openaire +3 more sources