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Found 16 papers, 12 papers with code
FCE-YOLOv8: YOLOv8 with Feature Context Excitation Modules for Fracture Detection in Pediatric Wrist X-ray Images
Children often suffer wrist trauma in daily life, while they usually need radiologists to analyze and interpret X-ray images before surgical treatment by surgeons.
YOLOv8-ResCBAM: YOLOv8 Based on An Effective Attention Module for Pediatric Wrist Fracture Detection
Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases.
Efficient GANs for Document Image Binarization Based on DWT and Normalization
To overcome this problem, this work introduces an efficient GAN method based on the three-stage network architecture that incorporates the Discrete Wavelet Transformation and normalization to reduce the input image size, which in turns, decrease both training and inference times.
Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection
Children often suffer wrist injuries in daily life, while fracture injuring radiologists usually need to analyze and interpret X-ray images before surgical treatment by surgeons.
Ranked #4 on
Object Detection
on GRAZPEDWRI-DX
FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method
Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life.
YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images
The introduction of YOLOv9, the latest version of the You Only Look Once (YOLO) series, has led to its widespread adoption across various scenarios.
Ranked #6 on
Object Detection
on GRAZPEDWRI-DX
YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection
Therefore, we combine ResBlock and GAM, introducing ResGAM to design another new YOLOv8-AM model, whose mAP 50 value is increased to 65. 0%.
Ranked #5 on
Object Detection
on GRAZPEDWRI-DX
Semantic Segmentation Using Super Resolution Technique as Pre-Processing
Combining high-level and low-level visual tasks is a common technique in the field of computer vision.
CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization
This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets.
Ranked #3 on
Binarization
on DIBCO 2013
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
We apply this strategy to SwinIR and present a new model, which named SwinOIR (Object Image Restoration Using Swin Transformer).
Ranked #12 on
Image Super-Resolution
on BSD100 - 3x upscaling
Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks
In Stage-2, we use four independent generators to separately train GAN models based on the four channels on the processed patch images to extract color foreground information.
Ranked #2 on
Binarization
on H-DIBCO 2018
Connection Reduction of DenseNet for Image Recognition
Baseline is a densely connected network, and the networks connected by the two new algorithms are named ShortNet1 and ShortNet2 respectively.
Ranked #2 on
Image Classification
on SVHN
(Percentage correct metric)
Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification
However, due to the limitation of computing power, deep learning algorithms are usually not available on mobile devices.
Ranked #3 on
Image Classification
on SVHN
(Percentage correct metric)
Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy for Image Recognition without Convolutions
This proves that Transformer has a good prospect in the field of image recognition.
Ranked #231 on
Image Classification
on CIFAR-10
ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce Connections
However, this compression method may result in a decrease in the model accuracy and an increase in the parameters and model size.
Ranked #4 on
Image Classification
on SVHN
(Percentage correct metric)
New Pruning Method Based on DenseNet Network for Image Classification
This work employs this method to connect blocks of different depths in different ways to reduce the usage of memory.
Ranked #215 on
Image Classification
on CIFAR-10
