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The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale cooccurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%. [ABSTRACT FROM AUTHOR]

In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm's strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches. [ABSTRACT FROM AUTHOR]

The goal of the work described here was to construct a deep learning–based intelligent diagnostic model for ophthalmic ultrasound images to provide auxiliary analysis for the intelligent clinical diagnosis of posterior ocular segment diseases. The InceptionV3–Xception fusion model was established by using two pre-trained network models—InceptionV3 and Xception—in series to achieve multilevel feature extraction and fusion, and a classifier more suitable for the multiclassification recognition task of ophthalmic ultrasound images was designed to classify 3402 ophthalmic ultrasound images. The accuracy, macro-average precision, macro-average sensitivity, macro-average F1 value, subject working feature curves and area under the curve were used as model evaluation metrics, and the credibility of the model was assessed by testing the decision basis of the model using a gradient-weighted class activation mapping method. The accuracy, precision, sensitivity and area under the subject working feature curve of the InceptionV3–Xception fusion model on the test set reached 0.9673, 0.9521, 0.9528 and 0.9988, respectively. The model decision basis was consistent with the clinical diagnosis basis of the ophthalmologist, which proves that the model has good reliability. The deep learning–based ophthalmic ultrasound image intelligent diagnosis model can accurately screen and identify five posterior ocular segment diseases, which is beneficial to the intelligent development of ophthalmic clinical diagnosis. [ABSTRACT FROM AUTHOR]

To solve the problem of robustness of encrypted medical image watermarking algorithms, a zero watermarking algorithm based on the discrete cosine transform (DCT) and an improved DarkNet53 convolutional neural network is proposed. The algorithm targets medical images in the encrypted domain. In this algorithm, DCT is performed on the encrypted medical image to extract 32-bit features as feature 1. DarkNet53, a pre-trained network, was chosen for migration learning for the network model. The network uses a fully connected layer and a regression layer instead of the original Softmax layer and classification layer, changing the original classification network into a regression network with an output of 128. With these transformations, 128-bit features can be extracted from encrypted medical images by this network, and then DCT is performed to extract 32-bit features as feature 2. The fusion of features 1 and 2 can effectively improve the robustness of the algorithm. The experimental results show that the algorithm can accurately distinguish different encrypted medical images and can effectively restore the original information from the encrypted watermarked information under traditional and geometric attacks. Compared with other algorithms, the proposed method demonstrates better robustness and invisibility. [ABSTRACT FROM AUTHOR]

In this paper, we construct models for convolutional neural networks and migration learning, conduct in-depth research on rice pest recognition methods, and design a plan based on convolutional neural networks and migration learning. The weight parameters obtained from the VGG16 model trained on the image dataset Image Net are migrated to recognize rice pests through the migration learning method. The convolutional and pooling layers of VGG16 are used as feature extraction layers. In contrast, the top layer is redesigned as a global average pooling layer and a SoftMax output layer, and some of the convolutional layers are frozen during training. The proposed method effectively improves the recognition accuracy of water to pest images and significantly reduces the number of model parameters. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy, slow speed and low defect identification of industrial site fabric defect detection, a improved fabric defect detection algorithm based on ResNet50 network is proposed. Firstly, the data set was preprocessed, and the model training set was enhanced by cutting the data samples, including the fault-free and 8 common defect categories. Then, the structure of Resnet50 network was improved to extract the weight parameters pre-trained on the large data set ImageNet for transfer learning. Finally, the defect detection and recognition model obtained by hyper parameter training was adjusted repeatedly. The results of several groups of comparative experiments show that the recognition accuracy of the improved model for normal fabric and 8 common defects reaches 96. 32%, which is 4.2% higher than that of the standard model, and the speed is doubled. The test in different fabric defect data sets has the best comprehensive performance, strong generalization ability and good robustness, which can meet the requirements of fabric defect detection in industrial production sites. [ABSTRACT FROM AUTHOR]

Accurate detection of flames is an important prerequisite for the implementation of many security measures. To address the problems of low detection accuracy and difficulty in detecting small target flame in the current mainstream flame detection algorithms, we propose a small target flame detection algorithm incorporating an attention mechanism. First, we optimize the self-acquired dataset with data enhancement and structural similarity algorithms to improve the quality of the dataset and enhance the training efficiency of the model; then, a coordinate attention mechanism is introduced in the feature extraction stage of the original you-only-look-once (YOLOv7) network to improve the ability of the model to extract useful feature information and help the model to more accurately; and then, a small target flame detection layer is introduced at the output of the YOLOv7 network to improve the model's ability to detect small flames. Finally, the parameters of each layer of the improved model are initialized through model pre-training and migration learning to reduce the risk of model overfitting and accelerate the convergence speed of model training. The experimental results show that the average detection accuracy of the flame detection algorithm designed in this paper reaches up to 89.5%, which is 8.7% better than the detection accuracy of the original YOLOv7 algorithm and is innovative and practical. [ABSTRACT FROM AUTHOR]

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomatoes and the plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rates and poor generalizations of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network (CNN) has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and a K-means clustering method was used to adjust more appropriate anchor sizes than manual setting, to improve detection accuracy. The test results showed that the mean average precision (mAP) was significantly improved compared with the traditional Faster R-CNN model. The training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of a precise targeting pesticide application system and an automatic picking device. [ABSTRACT FROM AUTHOR]