An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

Yao, Shangjie; Chen, Yaowu; Tian, Xiang; Jiang, Rongxin; Ma, Shuhao

doi:10.3390/app10051818

Cited by 23 publications

(10 citation statements)

References 20 publications

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“…To capture more contextual information at multiple scales, Lu et al [49] and Xia et al [14] set the dilation rates as 1, 3, and 5 to extract richer features. DeepLabv2 [20] (rate = 6, 12, 18, 24) and Yao et al [50] proposed PYolo to use multi-branch convolution with dilation rates of 1, 3, 6, and 12 for detecting pneumonia; these features complement each other to ensure that the information distributed in different ranges can be sampled [51].…”

Section: Dilated Convolutionmentioning

confidence: 99%

CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

Zhou

Chang

et al. 2022

Applied Sciences

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Convolutional neural networks (CNNs) have achieved great success in image classification tasks. In the process of a convolutional operation, a larger input area can capture more context information. Stacking several convolutional layers can enlarge the receptive field, but this increases the parameters. Most CNN models use pooling layers to extract important features, but the pooling operations cause information loss. Transposed convolution can increase the spatial size of the feature maps to recover the lost low-resolution information. In this study, we used two branches with different dilated rates to obtain different size features. The dilated convolution can capture richer information, and the outputs from the two channels are concatenated together as input for the next block. The small size feature maps of the top blocks are transposed to increase the spatial size of the feature maps to recover low-resolution prediction maps. We evaluated the model on three image classification benchmark datasets (CIFAR-10, SVHN, and FMNIST) with four state-of-the-art models, namely, VGG16, VGG19, ResNeXt, and DenseNet. The experimental results show that CDTNet achieved lower loss, higher accuracy, and faster convergence speed in the training and test stages. The average test accuracy of CDTNet increased by 54.81% at most on SVHN with VGG19 and by 1.28% at least on FMNIST with VGG16, which proves that CDTNet has better performance and strong generalization abilities, as well as fewer parameters.

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Section: Dilated Convolutionmentioning

confidence: 99%

CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

Zhou

Chang

et al. 2022

Applied Sciences

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“…Although deep semantic information can help accurately detect targets, shallow detailed information can improve detection accuracy to a certain extent. Therefore, only the feature maps on the last few deep convolutional layers are used for prediction, but the shallower features are ignored, which will reduce the detection performance [18]. Therefore, the four detection scales designed include both deep semantic feature information and shallower detail information, and the information between the detection scales are merged so that the network can obtain more and more robust image information.…”

Section: Improve Multi-scale Predictionmentioning

confidence: 99%

“…The average detection time of this model was 0.06 s per frame at 1920 × 1080 resolution. Tian et al [18] proposed an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. However, the deep learning methods are relatively slow as they need more computational resources that economical industrial computers usually cannot offer.…”

Section: Introductionmentioning

confidence: 99%

Fast recognition method for citrus under complex environments based on improved YOLOv3

Xiao

Huang

et al. 2021

The Journal of Engineering

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Here, the improved multi-scale YOLO algorithm (Improved-YOLOv3) is presented, which was proposed to realize fast and accurate recognition of citrus fruit in a field environment. With the modification of the YOLO-styled network model, a darknet-53 backbone network with residual modules was designed. A multi-scale detection module was to construct a network model for rapid recognition of citrus fruit in complex environments. Using the improved model to detect and identify citrus fruit targets, the network model can extract more feature information. The improved YOLOv3 model was tested with citrus data, and the detection performance of the improved network and the influence of the number of backbone network layers on the feature extraction capability were compared. The results showed a good detection ability (detection rate, accuracy, map, detection speed) for the target fruit, and the improved YOLOv3 network showed higher accuracy. Moreover, the performance of different training models were compared: the Improved-YOLOv3 has stronger robustness, higher detection accuracy, and shorter training time, and can recognize citrus in complex field environments. The experiment showed that the precision of Improved-YOLOv3 was 90.5% and the accuracy reached 94.3%, the recall rate was 90.3%, the detection time was 9.89 ms per frame, which could provide technical support for this visual recognition system of citrus picking robot.

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“…YOLO (You Only Look Once) is the first single-stage target detection algorithm that has achieved good results in detection accuracy and detection speed. It has been successfully applied in agriculture [31,32], geology [33], remote sensing [34,35] and medicine [36], and other fields. In addition, it is also widely used in the field of transportation, such as traffic sign detection [37], traffic flow detection [38], pavement pit detection [39], and visual crack detection [40].…”

Section: Introductionmentioning

confidence: 99%

Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm

Yan

Liu

et al. 2022

Advances in Civil Engineering

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Deep learning has achieved good results in the crack detection of roads and bridges. However, the timber structures of ancient architecture have strong orthotropic anisotropy and complex microscopic structures, and the law of cracks development is extremely complex. The image data has a large proportion of pixels, which is obviously different from the background gray value, and there is timber grain noise, thus the existing methods cannot accurately extract the complex texture contour feature of cracks. In previous studies, we have verified that YOLO v5s is effective in crack detection in timber structures of ancient architecture. However, there are many different versions of YOLO series models. In order to find a better algorithm, this paper mainly adopts three models including YOLO v3, YOLO v4s-mish, and YOLO v5s to detect cracks in the timber structures of ancient architecture, and compares and analyzes the advantages and disadvantages of the three models. In the comparing process, we mainly have discussed the index performance of the three models in terms of training time, loss function, recall rate, and mAP value. We have summarized and analyzed the advantages and disadvantages of the three models in cracks detection of the timber structures of ancient architecture, and concluded the comparing results of the three models in cracks detection based on experiments. We published the first picture data set of cracks in timber structures of ancient architecture, and applied YOLO model in the intelligent identification field of cracks in timber structures of ancient architecture for the first time, which opened up a new idea for the intelligent operation and maintenance of the timber structures of ancient architecture.

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An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

Cited by 23 publications

References 20 publications

CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

Fast recognition method for citrus under complex environments based on improved YOLOv3

Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm

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