Deep Learning for Highly Accurate Hand Recognition Based on Yolov7 Model

Dewi, Christine; Chen, Abbott Po Shun; Christanto, Henoch Juli

doi:10.3390/bdcc7010053

Cited by 26 publications

(15 citation statements)

References 38 publications

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“…The seed quality detection methods such as germination and staining techniques are time-consuming and rely heavily on human intervention, which may lead to inaccurate results due to human error. In order to develop an automated and standardized method for detecting seed germination that is efficient, accurate, and reliable, the YOLOv7 ( Wang et al., 2022b ) object detection algorithm was selected in this study, which is one of the most widely used algorithms for object detection since its release in 2015 ( Dewi et al., 2023 ). YOLOv7 is a real-time object detection algorithm ( Soeb et al., 2023 ), which has evolved from YOLOv5 and has faster inference speed, improved detection accuracy, and reduced computational complexity.…”

Section: Methodsmentioning

confidence: 99%

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

Fan,

An,

Wang

et al. 2023

Front. Plant Sci.

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The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this study, hyperspectral imaging (HSI) technology was used to obtain images and spectral information of maize seeds with different aging stages. To reduce data input and improve model detection speed while obtaining more stable prediction results, successive projections algorithm (SPA) was used to extract key wavelengths that characterize seed viability, then key wavelength images of maize seed were divided into small blocks with 5 pixels ×5 pixels and fed into a multi-scale 3D convolutional neural network (3DCNN) for further optimizing the discrimination possibility of single-seed viability. The final discriminant result of single-seed viability was determined by comprehensively evaluating the result of all small blocks belonging to the same seed with the voting algorithm. The results showed that the multi-scale 3DCNN model achieved an accuracy of 90.67% for the discrimination of single-seed viability on the test set. Furthermore, an effort to reduce labor and avoid the misclassification caused by human subjective factors, a YOLOv7 model and a Mask R-CNN model were constructed respectively for germination judgment and bud length detection in this study, the result showed that mean average precision (mAP) of YOLOv7 model could reach 99.7%, and the determination coefficient of Mask R-CNN model was 0.98. Overall, this study provided a feasible solution for detecting maize seed viability using HSI technology and multi-scale 3DCNN, which was crucial for large-scale screening of viable seeds. This study provided theoretical support for improving planting quality and crop yield.

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Section: Methodsmentioning

confidence: 99%

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

Fan,

An,

Wang

et al. 2023

Front. Plant Sci.

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“…Overall, the channel attention mechanism plays a crucial role in optimizing the representation and learning capabilities of CNNs, enabling them to extract and emphasize relevant features for improved object recognition and classification (Dewi et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Peng

2023

Front. Neurosci.

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IntroductionWith the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.MethodsThe proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.ResultsExperimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.DiscussionIn conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.

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“…The feature extraction is mainly through the convolutional neural network to extract the features of each image. Each layer of the convolutional neural network is based on multiple convolution kernels and activation functions [19,20] (RelU). The convolution kernel is mainly used To extract the features of the image, learn and store them, the activation function reflects the obtained features into a meaningful feature map.…”

Section: Deep Neural Network：yolov7mentioning

confidence: 99%

Convolutional neural network-based crack identification and detection in underwater buildings

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical Systems

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Underwater cracks are a common problem in buildings and infrastructure, and their presence can have a significant impact on the stability and safety of the structure. Therefore, early detection and repair of cracks is very important. However, traditional underwater crack detection methods usually require manual involvement, which is time-consuming and dangerous. Therefore, it is of great significance to develop an automated crack identification and detection system that can be mounted on an underwater robot. For traditional underwater crack detection, the obtained underwater video is usually segmented and preprocessed, and the obtained data is input into a computer vision-based system for crack recognition and detection, which is much less efficient than the current deep learning methods. In this paper, two kinds of underwater building crack recognition network and detection network based on convolutional neural network are proposed respectively, by collecting different light, depth, turbidity and other multi-waters video materials, after a series of computer vision processing, it will be input into the neural network for the expansion of the data set, and the obtained data set is divided into test set, training set and validation set, and the training set will be input into the neural network used for the target recognition to be trained, to get the comprehensive recognition model of cracks in underwater complex environment. The size and shape of the cracks are manually depicted using the training set, and inputted into the neural network for crack detection to obtain the complex background underwater crack detection model. Using the complex background underwater crack detection model to detect the underwater environment through the neural network for target recognition, the underwater robot equipped with a waterproof and anti-shaking camera communicates with the shore and moves, follows the recognition results of the neural network for target recognition to lock the crack location, and then manipulates the underwater robot to approach the target crack, and then uses the neural network for target recognition to detect the underwater crack with a complex background using the complex background underwater crack detection model. The neural network for target recognition uses a complex background underwater crack detection model to segment the scaled underwater video captured by the camera and process it in high-definition to map the length and width of the cracks with pixel-level accuracy, and then transmits the data to the shore in real time to complete the identification and detection of cracks in underwater buildings.

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Deep Learning for Highly Accurate Hand Recognition Based on Yolov7 Model

Cited by 26 publications

References 38 publications

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Convolutional neural network-based crack identification and detection in underwater buildings

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