Results 11 to 20 of about 1,485,416 (350)
Improved Small Object Detection Algorithm CRL-YOLOv5. [PDF]
Sensors (Basel)Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed
Wang Z +6 more
europepmc +5 more sources
Toward Versatile Small Object Detection with Temporal-YOLOv8. [PDF]
Sensors (Basel)Deep learning has become the preferred method for automated object detection, but the accurate detection of small objects remains a challenge due to the lack of distinctive appearance features. Most deep learning-based detectors do not exploit the temporal information that is available in video, even though this context is often essential when the ...
van Leeuwen MC +4 more
europepmc +6 more sources
Small Object Detection and Tracking: A Comprehensive Review
Sensors, 2023 Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation, identification of significant events, and detection of abnormal activities ...
Behzad Mirzaei +3 more
semanticscholar +7 more sources
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. The detection models can get better results for big object.
Guo X. Hu +4 more
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Deep learning-based small object detection: A survey
Mathematical Biosciences and Engineering, 2023 <abstract> <p>Small object detection (SOD) is significant for many real-world applications, including criminal investigation, autonomous driving and remote sensing images. SOD has been one of the most challenging tasks in computer vision due to its low resolution and noise representation.
Qihan Feng, Xinzheng Xu, Zhixiao Wang
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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. At present, many studies have made significant progress in improving the detection accuracy of small objects. However, some of them cannot balance the detection speed and accuracy well.
Li Li, Bingxue Li, Hongjuan Zhou
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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.
WANG Xiaoqiang LI Kecen
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Dataset for small object detection with shadow (SODwS). [PDF]
Data BriefDetecting small objects in aerial images poses several challenges, including issues with resolution limitations, scale variability, background clutter, and object occlusion. Annotated datasets for small objects in aerial images are often scarce, complicating the training and validation of detection models.
Mat-Desa S +8 more
europepmc +3 more sources
Cross-Layer Feature Pyramid Transformer for Small Object Detection in Aerial Images [PDF]
IEEE Transactions on Geoscience and Remote SensingObject detection in aerial images has always been a challenging task due to the generally small size of the objects. Most current detectors prioritize the development of new detection frameworks, often overlooking research on fundamental components such ...
Zewen Du +4 more
openalex +2 more sources
Swinvision: Detecting Small Objects in Low-Light Environments
IEEE AccessNeural networks have been widely employed in the field of object detection. Transformers enable effective object detection through global context awareness, modular design, scalability, and adaptability to diverse target scales. However, small object detection requires careful consideration due to its comprehensive computations, data requirements, and ...
Tao Dai +3 more
openaire +3 more sources