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. This paper is the first to apply the YOLOv9 algorithm model to the fracture detection task as computer-assisted diagnosis (CAD) to help radiologists and surgeons to interpret X-ray images. Specifically, this paper trained the model on the GRAZPEDWRI-DX dataset and extended the training set using data augmentation techniques to improve the model performance. Experimental results demonstrate that compared to the mAP 50-95 of the current state-of-the-art (SOTA) model, the YOLOv9 model increased the value from 42.16% to 43.73%, with an improvement of 3.7%. The implementation code is publicly available at https://github.com/RuiyangJu/YOLOv9-Fracture-Detection.

YOLOv8-AM: YOLOv8 with Attention Mechanisms 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. Before performing surgery, surgeons often request patients to undergo X-ray imaging first and prepare for it based on the analysis of the radiologist. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture detection as computer-assisted diagnosis (CAD). In 2023, Ultralytics presented the latest version of the YOLO models, which has been employed for detecting fractures across various parts of the body. Attention mechanism is one of the hottest methods to improve the model performance. This research work proposes YOLOv8-AM, which incorporates the attention mechanism into the original YOLOv8 architecture. Specifically, we respectively employ four attention modules, Convolutional Block Attention Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Shuffle Attention (SA), to design the improved models and train them on GRAZPEDWRI-DX dataset. Experimental results demonstrate that the mean Average Precision at IoU 50 (mAP 50) of the YOLOv8-AM model based on ResBlock + CBAM (ResCBAM) increased from 63.6% to 65.8%, which achieves the state-of-the-art (SOTA) performance. Conversely, YOLOv8-AM model incorporating GAM obtains the mAP 50 value of 64.2%, which is not a satisfactory enhancement. Therefore, we combine ResBlock and GAM, introducing ResGAM to design another new YOLOv8-AM model, whose mAP 50 value is increased to 65.0%.

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. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance the results and performance of semantic segmentation.

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. 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. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods.

A Framework for Real-time Object Detection and Image Restoration

Object detection and single image super-resolution are classic problems in computer vision (CV). The object detection task aims to recognize the objects in input images, while the image restoration task aims to reconstruct high quality images from given low quality images. In this paper, a two-stage framework for object detection and image restoration is proposed. The first stage uses YOLO series algorithms to complete the object detection and then performs image cropping. In the second stage, this work improves Swin Transformer and uses the new proposed algorithm to connect the Swin Transformer layer to design a new neural network architecture. We name the newly proposed network for image restoration SwinOIR. This work compares the model performance of different versions of YOLO detection algorithms on MS COCO dataset and Pascal VOC dataset, demonstrating the suitability of different YOLO network models for the first stage of the framework in different scenarios. For image super-resolution task, it compares the model performance of using different methods of connecting Swin Transformer layers and design different sizes of SwinOIR for use in different life scenarios. Our implementation code is released at https://github.com/Rubbbbbbbbby/SwinOIR.

Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks

The efficient segmentation of foreground text information from the background in degraded color document images is a hot research topic. Due to the imperfect preservation of ancient documents over a long period of time, various types of degradation, including staining, yellowing, and ink seepage, have seriously affected the results of image binarization. In this paper, a three-stage method is proposed for image enhancement and binarization of degraded color document images by using discrete wavelet transform (DWT) and generative adversarial network (GAN). In Stage-1, we use DWT and retain the LL subband images to achieve the image enhancement. In Stage-2, the original input image is split into four (Red, Green, Blue and Gray) single-channel images, each of which trains the independent adversarial networks. The trained adversarial network models are used to extract the color foreground information from the images. In Stage-3, in order to combine global and local features, the output image from Stage-2 and the original input image are used to train the independent adversarial networks for document binarization. The experimental results demonstrate that our proposed method outperforms many classical and state-of-the-art (SOTA) methods on the Document Image Binarization Contest (DIBCO) dataset. We release our implementation code at https://github.com/abcpp12383/ThreeStageBinarization.

Connection Reduction Is All You Need

Convolutional Neural Networks (CNN) increase depth by stacking convolutional layers, and deeper network models perform better in image recognition. Empirical research shows that simply stacking convolutional layers does not make the network train better, and skip connection (residual learning) can improve network model performance. For the image classification task, models with global densely connected architectures perform well in large datasets like ImageNet, but are not suitable for small datasets such as CIFAR-10 and SVHN. Different from dense connections, we propose two new algorithms to connect layers. Baseline is a densely connected network, and the networks connected by the two new algorithms are named ShortNet1 and ShortNet2 respectively. The experimental results of image classification on CIFAR-10 and SVHN show that ShortNet1 has a 5% lower test error rate and 25% faster inference time than Baseline. ShortNet2 speeds up inference time by 40% with less loss in test accuracy.

TripleNet: A Low Computing Power Platform of Low-Parameter Network

With the excellent performance of deep learning technology in the field of computer vision, convolutional neural network (CNN) architecture has become the main backbone of computer vision task technology. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. This paper proposes a lightweight convolutional neural network model, TripleNet, an improved convolutional neural network based on HarDNet and ThreshNet, inheriting the advantages of small memory usage and low power consumption of the mentioned two models. TripleNet uses three different convolutional layers combined into a new model architecture, which has less number of parameters than that of HarDNet and ThreshNet. CIFAR-10 and SVHN datasets were used for image classification by employing HarDNet, ThreshNet, and our proposed TripleNet for verification. Experimental results show that, compared with HarDNet, TripleNet's parameters are reduced by 66% and its accuracy rate is increased by 18%; compared with ThreshNet, TripleNet's parameters are reduced by 37% and its accuracy rate is increased by 5%.