Reviewerly/exHarmony huggingface dataset:
Design and implementation of video recording storage matrix based on domestic embedded SOC

In order to meet the needs of monitoring, intelligent life, simulation training and other scenarios to study, storage, and playback of multi -source multi -made sensors, the current mainstream video codec chip has been studied, and the embedded video codec SOC FH36 is selected as the core chip as the core chip The audio and video codec and decoding platform of 16 multi-source multi-source sensors, video matrix stitching output, and H.264 coding are designed. After completing the design, fully test and verify the power consumption, delay, definition, compression rate, reliability, etc. based on FH36 audio and video codec and other aspects. Through continuous optimization, the reliability, stability, and functional performance of the audio and video codec platform have been further improved. Unilateral coding decoding 1080p@30FPS can reach 5.8Mbps, and the encoding system delay can be controlled at 1080p@30fps at 1080p@30fps can be controlled at 1080p@30fps at 1080p@30fps can be controlled at 1080p@30FPS at 1080p@30fps at 1080p@30fps can be controlled at 1080p@30fps at 1080p@30FPS. Within 100ms. Based on the embedded SOC video record storage matrix, it can achieve compressed storage of up to 16 -way sensor video information. At the same time, the video decompress stability, the screen is clear, no jitter, the system function and performance have achieved the expected goals, and mass production has been achieved.
External attention augmented Swin-UNet network for semi-supervised cardiac image segmentation

Cardiac image segmentation is an important step in clinical diagnosis of heart diseases. To this day, CNNs based UNet-like networks have been applied successfully in cardiac image segmentation, yet they still fail in taking full advantage of global semantic information due to the limits of the convolution operation. On the other hand, annotation of cardiac images is a time-consuming and laborious task and requires the participation of experts. In this paper, we proposed an External Attention Augmented SwinUNet (EA-SwinUNet) network for semi-supervised cardiac image segmentation. First, we proposed a novel mean teacher semi-supervised architecture to cardiac segmentation network. Second, to overcome the limits of CNNs, we designed an improved SwinUNet segmentation network to leverage the global semantic features. By introducing external attention mechanism in skip connections, it can help network overcome the accuracy impact of small datasets and enhance the feature association between global samples. To validate our EA-SwinUNet, we conducted several experiments on the ACDC dataset and our network achieves a Dice score of 77.31% and a 95%HD of 7.03.
Freeway vehicle detection on improved YOLOv7

Vehicle detection is the important basis for smart traffic. In order to further improve the performance of vehicle detection in freeway scenes, an improved YOLOv7 network is proposed in this paper. Firstly, vehicle dataset is created via cameras mounted on freeway. Secondly, Improved data augmentation method is applied to simulated the special environment interference. Then, SimAM attention mechanism module is inserted into YOLOv7, and the optimal adding position is obtained by discussing the performance of different attention composition. Finally, we provide experimental results to verify the effectiveness of the improvement. The results show that method proposed in this paper increase the mAP by 11% than original YOLOv7 on our dataset.
Unsupervised aircraft detection in SAR images with image-level domain adaption from optical images

Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in both civil and military fields. However, due to the special imaging mechanism of SAR images, the aircraft annotating process is easily affected by interferences and noises in the background, leading to a high labeling cost. As most object detection networks are trained in a supervised manner, a serious problem of applying them to SAR aircraft detection tasks is the insufficient training data. To address this problem, we propose an unsupervised domain adaption method for the training of SAR aircraft detectors. First, we propose to transfer knowledge from optical aerial images in which aircraft annotations are easier to obtain. By adopting an image-level domain adaption, the target information in optical images can be utilized for the training of SAR aircraft detectors. Then, CycleGAN is adopted to overcome the discrepancy between optical and SAR domains by image-style translation. To evaluate the effectiveness of the proposed method, we build up an optical-to-SAR aircraft detection dataset (O2SADD) based on existing public datasets. Experiments on O2SADD indicate that the proposed method can significantly improve the performance of SAR aircraft detectors without manually annotating aircraft in SAR images.
Research on global illumination exploration in dynamic scenes

Neural rendering can achieve satisfactory illumination effects with unknown light source locations and brightness. When training on dynamic scenarios (where object positions, textures, lighting, and viewpoints can vary), variations in object position, material properties, light intensity, and angle make the training results unsatisfactory due to the influence of global light sources. This paper proposes a novel global illumination exploration method, which uses Markov Chain Monte Carlo (MCMC) to perform small sample sampling of light points, and incorporates a new Bayesian optimization (Randomized Search) strategy to optimize the sample data combined with real-time data to remove sampling redundant information and improve training efficiency. The comprehensive experimental results show that our proposed method provides a practical and effective solution for the study of global illumination in dynamic scenes.
Improved clothing segmentation algorithm based on Deeplabv3 + network

In recent years, the fashion industry has developed rapidly, and the image is one of the main carriers for people to obtain clothing information. Therefore, the analysis and processing of clothing image through the technology in the field of artificial intelligence has become a major research content in the field of clothing development. Image segmentation technology can be used to segment different clothing regions from the clothing image in order to facilitate the subsequent analysis and processing of clothing image, which is one of the basic research directions of clothing image analysis. At present, most of the methods belong to the traditional segmentation methods, or are based on the deep convolutional neural network (DCNN). Especially for Deeplab, FCN and their improved network segmentation models, although these network models have different degrees of improvement compared with the traditional methods, there are still some problems such as the fitting degree of clothing region segmentation is not high and the effect of clothing edge segmentation is poor. To solve these problems, this paper adds object context information extraction module and edge optimization module based on Deeplab v3+ network. The object context information extraction module focuses on the context information aggregation method in semantic segmentation, and enhances the feature representation of pixel points with the help of the feature information of the corresponding clothing region, so as to improve the label classification effect of pixel points in the clothing image and improve the fitting degree of clothing region segmentation. The edge optimization module makes the pixels in the edge area directly use the label prediction of the pixels in the same category area to greatly improve the edge segmentation effect. Our method performs well on the DeepFashion-MultiModal dataset, and experiments show that the improved Deeplabv3 + network outperforms the original Deeplabv3 + network and other popular segmentation methods, with an excellent mIOU of 67. 29%.
An overview of Chinese fine-grained sentiment analysis methods based on multi-kernel plant intelligence model

This paper proposes a new model, PGB_GPT, for Chinese fine-grained sentiment analysis, which combines Bidirectional Long Short-Term Memory (BiLSTM), Graph Convolutional Neural Network (GCN), and Generative Pre-Training Model (GPT). Additionally, a multi-core plant intelligent model is introduced to extract comprehensive symbolic meaning and improve the precision and accuracy of sentiment analysis. PGB_GPT outperforms other combination models and the possibility of merging a multi-core plant intelligence model with BiLSTM, GCN, and GPT for more extensive and accurate emotion analysis is highlighted. For Chinese fine-grained sentiment analysis, the PGB_GPT model combines BiLSTM, GCN, and GPT, with "P" representing "Plant Intelligence," "G" representing "Graph Convolutional Neural Network," "B" representing "Bidirectional Long Short-Term Memory," and "GPT" representing "Generative Pre-Training Model." As evidenced by the sentiment analysis dataset evaluation, each component greatly contributes to the model's enhanced performance.
An efficient harbor ship detection method based on deep learning in high-resolution remote sensing images

With the increasing prosperity of shipping, effective harbor management has become a requirement. High-resolution optical remote sensing images can effectively monitor and manage ships due to their rich spatial information, clear geometric information, and unique top-down view. However, harbor scenes usually have complex background information and densely distributed ship targets, these two factors increase the difficulty of the algorithm to detect ship targets in remote sensing harbor scenes. With respect to these two issues, this paper proposes a novel harbor ship detection method based on YOLOv6-s with finer-feature fusion which is called F-YOLOv6. First, Extract ship targets from complex ground information. By importing the backbone extraction network efficient re-parameterization (Efficient-Rep) of YOLOv6-s, its efficient extraction ability can help the algorithm to improve the detection accuracy. Second, to improve the detection performance of the algorithm for densely arranged ship targets, the finer pixel aggregation network (F-PAN) is constructed to obtain more ship object position information, and on this basis, it is integrated with the deep high-level semantic information to improve the robustness of the algorithm. Third, to further improve the timeliness of the algorithm, the detection efficiency of the algorithm is improved by deleting some redundant prediction headers. Finally, extensive experiments are carried out on the harbor ship detection (HSD) dataset, which is suitable for densely arranged ship detection, to verify the effectiveness of the proposed method. In addition, the method is verified by the DIOR public dataset, and its generalization is better than other benchmark algorithms.
DAT-SegNet: a medical multi-organ segmentation network based on deformation attention and transformer

With the development of Transformer and its derivatives over the last two years, several research have integrated Transformer with CNN or in instead of CNN to advance medical image segmentation. Although they often create acceptable feature maps for large organs, segmentation accuracy for small organs is less than satisfactory. Transformer excels at global context, but it displays limits in capturing fine-grained features, especially in medical areas, because convolution is unable to describe long-term correlations prevalent in images. This is because local information modeling lacks a spatial inductive bias. Currently available Transformer-based segmentation networks are not often optimized for this issue, we therefore use transformer as the backbone and provide a medical image segmentation network with deformation attention. The model uses attention mechanism to boost the impact of feature maps to address the issue of low accuracy of tiny organ segmentation in multi-organ segmentation tasks. On the Synapse dataset, our model has so far produced SOTA results.
Facial expression optimal class separability for expression recognition

Facial Expression Recognition (FER) is a hard work, but in the current research on expression recognition technique, the regression of recognition performance is caused by misclassification problem which is caused by non-saliency of local features for facial image. In this paper, we introduce a framework for FER using inter-class separability for regional-feature fusion based on multi-domain expression generation (GSER).We fuse the regional features from multiple layers, and the local feature from most informative regions is enhanced to improve the precision of expression classification. An inter-class separability method based on pre-defined evenly-distributed class centroids is discussed for loss function, which solve the misclassification problem of expressions. The framework is evaluated over different standard facial expression datasets including KDEF and RAF-DB, and experiments show that the problems for unbalance of sample size and misclassification are solved, the robustness and effectiveness of expression classification is improved.
Research on hand sketch recognition method based on Sketch-AlexNet

With the advancement of artificial intelligence and image processing technology, hand-drawn sketches can be utilized in diverse applications, such as digital creativity, design, and education, among others, highlighting their vast potential in various fields. However, existing challenges in hand-sketch recognition such as low accuracy and efficiency necessitate the development of an improved recognition method. To address these challenges, a sketch recognition method based on Sketch-AlexNet is proposed. Specifically, a larger first layer convolutional kernel is selected to enhance feature extraction ability, while a smaller step size is used to minimize information loss. Moreover, a set of multiplicative convolutional kernels is introduced to replace the original convolutional structure, enabling the network to extract features across shallow to deep hierarchies. The Sketch-AlexNet model is trained on a self-built hand-drawn sketch dataset, yielding a 9.8% accuracy improvement, better recognition results, and corresponding advancements in recognition speed and stability.
Research on intelligent vehicles directions selection algorithm based on deep learning (Withdrawal Notice)

With the continuous development of intelligent vehicles, the moving direction selection is becoming an essential issue for intelligent control. However, existing direction selection methods are concentrated on global dynamic programming or obstacle avoidance, which ignores the vision of intelligent vehicle combined with deep learning algorithm. In this paper, we present a novel direction selection through input moving images of intelligent vehicles. Through establishing the deep learning model, the system can automatically choose the moving direction for intelligent vehicles. With the continuous iterations of deep learning, the proposed model can achieve approximate 85 percentage of selection accuracy. The inner layers of deep learning model can get more understand about the vehicle directions and is able to select the correct direction for intelligent vehicles. From our extensive experimental simulation results, we can conclude that our proposed model can achieve high performance of disposing moving direction selection issue with reasonable response time through comparing with other existing method.
A Lightweight Method of Knowledge Graph Convolution Network for Collaborative Filtering

In recent years, knowledge-aware recommendation systems have gained popularity as a solution to address the challenges of data sparsity and cold start in collaborative filtering. However, traditional knowledge graph convolutional networks impose significant computational burdens during training, demanding substantial resources and increasing the cost of recommendations. To address this issue, this article proposes a lightweight knowledge graph convolutional network for collaborative filtering (LKGCF). LKGCF eliminates the feature transformation and nonlinear activation components, by focusing on essential elements such as neighborhood aggregation and layer combination. LKGCF captures the user's long-distance personalized interests on the knowledge graph by sampling from neighborhood information and constructing a weighted sum of item embeddings. Experimental results demonstrate that the proposed model is easy to train and implement due to its coherence and simplicity. Furthermore, notable improvements in recommendation performance are observed compared to strong baselines.
Research on user's facial expression analysis when watching TV

Facial expression is a basic way to express human emotions, and it is the primary medium for individuals to communicate with others. The display of facial expressions can better understand the other person's feelings and emotions. With the continuous development of artificial intelligence technology, the demand for human-computer interaction has also increased. The development of artificial intelligence plays an important role in face recognition and micro-expression changes when users watch IPTV. Because of the small range of motion and fast change of micro-expression, it is difficult to analyze it manually. Therefore, it is very necessary to develop a reliable recognition system. In this paper, the face recognition model facenet model is used to learn the image features of the face, Yolov5 and Yolox neural network are used as the basis to compare the models trained, and the performance of the selected model is tested to select the best effect.
Collision detection of sensorless cooperative robots based on long short term memory network

In man-machine cooperative environment, in order to ensure the safety of the cooperative robot and people, it is required that the cooperative robot can detect the collision quickly and accurately. Since the use of external sensors not only increases the complexity of cooperative robots, but also increases the cost, this paper proposes a collision detection method for cooperative robots without external sensors based on long and short term memory network (LSTM). Firstly, the dynamic trajectory is calculated by using the observation matrix of the dynamic model, and then the operating data of the cooperative robot is collected through the trajectory, and the dynamic parameters of the cooperative robot are obtained by using the least square method. Finally, the LSTM network model is designed to compensate the errors generated in the identification process of the dynamic parameters, and the results before and after the compensation are compared by experiments. The experimental results show that the recognition accuracy of the dynamic model can be effectively improved by the compensated results of the network model, and the collision detection accuracy of the cooperative robot can be effectively improved by comparing the actual torque with the theoretical torque calculated by the compensated dynamic model.
Segmentation of sandstone particles based on improved watershed algorithm

In order to better segment the sandstone particles, this paper improves the traditional image acquisition method by adding a baffle plate and a divider plate to collect the cohesive sandstone images. The collected images are processed by a series of OpenCV libraries in the computer, including grayscale and grayscale equalization, filtering noise reduction, binarization, morphological optimization and image segmentation based on the improved watershed algorithm. In the sandstone particle segmentation experiment, By comparing the results obtained by the image method with the results obtained by the manual screening method, the experimental results show that the image method proposed in this paper can effectively segment the real sandstone particles in the sandstone segmentation of the mixing production line, and the availability of this method is very high, meeting the industrial requirements.
Research on vehicle logo positioning method based on deep learning

Aiming at the problem of low efficiency and low precision of vehicle logo positioning, this paper proposes an improved vehicle logo positioning algorithm VLL-Net based on Yolov4. First, the Yolov4 network structure is improved, and the loss function of Yolov4 is modified, and the ResNet residual network structure is added between the convolutional layers before each YOLO output layer to solve the problem that CNN will follow the network during training. The problem of gradient disappearance occurs in the deepening of the algorithm. Focal Loss is used to solve the imbalance problem caused by too many negative samples during the algorithm training process, making the whole algorithm more suitable for the vehicle logo positioning task. The experimental results show that the improved yolov4 network model improves the detection accuracy.
An image classification method based on few-shot learning

Image classification technology is at the heart of many complex computer vision applications, including object tracking, video categorization, and action identification. In the area of picture classification, data-driven supervised learning has had considerable success, but these model's capacity to generalize is highly reliant on a huge amount of labeled data. Not all job contexts, though, may quickly acquire a sizable number of labeled dataset samples. This article builds a deep learning approach appropriate for few-sample image classification tasks utilizing Meta-Learning in order to address the issue of image classification tasks in scenarios with few samples. Finally, to confirm the success of the approach, comparative experiments are run on the handwritten letter data set from Omniglot.
Siamese tracker based on hybrid dilated convolution and global cross-correlation

Backbone networks and cross correlation operations are two important modules of the current mainstream Siamese network tracking framework. However, backbone networks based on dilated convolutions have a checkerboard effect while expanding the receptive field. The size of the depth-wise cross correlation features matching area and the number of channels activated affect the discrimination ability of the object boundary. Aiming at the problems of checkerboard effect and depth-wise cross correlation operation, proposes a Siamese tracker based on hybrid dilated convolution and global cross correlation, which uses hybrid expansion convolution to improve the checkerboard effect, improve the effectiveness of feature extraction, the global cross-correlation operation is used to promote the accuracy of the matching between the template feature and the search region feature. Evaluated on VOT2018, OTB100, UAV123 and other public test sets, the performance of this proposed method is better than that of mainstream Siamese network tracking algorithms, and the tracking effect is effectively improved.
DBA: downsampling-based adversarial attack in medical image analysis

The next-generation of artificial intelligence technology has contributed significantly to the development of medical intelligence. However, the widespread use of deep neural networks (DNNs) has also brought about serious security threats. In this paper, we present an adversarial attack approach for deep learning-based image segmentation models in the field of medical image analysis. In our solutions, we propose a novel adversarial attack method, which is designed to exploit the DNNs’ generic down-sampling operation to ensure the effectiveness, stealthiness, and transferability of the attack. We perform the attack on two State-Of-The-Art (SOTA) models, DDANet and CaraNet in a general medical image dataset Kvasir-SEG, and a comprehensive evaluation shows that our attack is effective stealthy, and transferrable.