Reviewerly/exHarmony huggingface dataset:
Occlusion Target Detection Based on Improved YOLOv5 Model

To solve the problem that the network is not robust due to the insufficient feature information extraction of occluded objects, an occluded objects detection algorithm combining coordinate attention (CA) mechanism and improved non-maximum suppression (NMS) is proposed in this paper. Based on the YOLOv5 model, the coordinate attention mechanism is first added into the feature extraction layer, which considers both feature information and position information to improve the problem of insufficient feature information extraction due to occlusion. Then, based on Generalized intersection over union (GIoU), the center distance and aspect ratio are considered, Complete-IOU (CIoU) is conducted to replace the original loss function to solve the degradation problem existing in GIoU and the problem that Distance-IoU (DIoU) does not consider aspect ratio. Finally, the traditional NMS only considers Intersection over union (IoU) as an indicator to remove duplicate boxes, which leads to error suppression, kCIoU-NMS is proposed instead of NMS to solve the shortcomings brought by IoU. Tested on a test set with different occlusion rates when the targets are occluded from each other and the background. The accuracy of the model in this paper is above 95% when the occlusion rate is 20% and 40%, and when the occlusion rate is 60%, the detection accuracy is 83.80% and 87.60%, which are 16.00% and 14.40% higher than the original model, respectively. The experimental results show that the method proposed in this paper has good effect and robustness.
A Lightweight Ship Detection Algorithm Based on YOLOV5

Aiming at the high complexity and large amount of computation of ship target recognition, this paper proposes a detection algorithm of lightweight ship based on YOLOV5. The EagleEye pruning is used to compress the model and improve the speed of inference. α-EIoU loss function is proposed to accelerate model convergence and improve the accuracy of detection. The model is only 1.97MB after pruning, and the detection speed can reach 238FPS, which can achieve the real-time ship target recognition.
Path Planning Method Using Dyna-Q Algorithm under Complex Urban Environment

Path planning and obstacle avoidance problems are now the focus of robotics research. This paper uses the Dyna-Q reinforcement learning algorithm to implement an obstacle avoidance and a path planning algorithm for unmanned ground vehicle(UGV) under urban environment. Using the reinforcement learning algorithm, we calculate the waypoints of the unmanned vehicle and achieve obstacle avoidance tasks and path planning using a vector field. Finally, we use a PID controller on unmanned aerial vehicle (UAV) to realize the air-ground collaboration task. The algorithms and the agents’ modeling in this paper are implemented in the lab’s simulation platform.
The study of the future two-dimensional game design: 2D versus 3D Graphics

This study examines both the advantages and disadvantages of two-dimensional and three-dimensional games. Using this examination, this article compares these two types of game graphics and discuss which one is superior, for the purpose of figuring out the future possibilities of two-dimensional games and try to answer the question - Will 2D games be replaced by 3D games entirely in the near future? In addition, I use a case study as methodology to analyse a classic 2D game, thereby presenting what the main focus of game development should be and what today’s games can learn from it.
End-to-End Reduced-Reference Quality Assessment for Single Image Super-Resolution using Convolutional Network

Single image super-resolution (SISR) reconstructs high resolution (HR) images from a single low resolution (LR) image and has been widely studied in recent years. However, the evaluation of SISR images remains a problem worth exploring. In this paper, we propose an reduced-reference method to assess the SISR images based on an end-to-end model. The proposed image quality assessment (IQA) metric takes the LR image as a reference and exploits the structure and texture information of images to quantify the degradations of SISR images, and predicts the quality of SISR images by a regression network. Experimental results on two public SR-IQA databases show that our method obtains the best evaluation results compared with other methods, which indicates the superiority of our method.
Single Object Tracking in Satellite Videos with Meta-updater and Knowledge Distillation

The subject of target tracking for satellite videos has received more and more attention. Owing to the development of convolutional neural network, it has been demonstrated that the trackers using deep convolutional features perform well on several object tracking benchmarks. However, it is difficult to achieve excellent results directly in the field of satellite video tracking as there are several challenges such as low resolution, complex backgrounds, limited edge information, many interfering targets, loss of occlusion, etc. Thus, in this paper, we introduce an offline-trained meta-updater into the deep learning tracker to guide the tracker’s update in complex situations. Furthermore, we utilize the idea of knowledge distillation to effectively enhance the feature representation, discrimination and localization abilities in tiny object tracking in satellite video. The experimental results on satellite video datasets show that our tracker outperforms other representative deep convolutional feature-based tracking methods in terms of accuracy and precision.
Multi-robot Cooperative Obstacle Avoidance Based on Improved Artificial Potential Field Method

Obstacle avoidance is important in the cooperative control of multi-robot system. Model predictive control (MPC) is used extensively in trajectory planning, in which a known global path is tracked with small tracking error. Due to the physical size and speed of the robot, dynamic obstacle avoidance should be included in MPC. This paper introduces an improved artificial potential field (APF) method to optimize the trajectory planning method based on MPC. Firstly, a fractional gravitational field is set at the target point to ensure that the robot reaches the target point. Secondly, a simplified repulsive force field is set up on the static obstacle, and it is segmented and linearized to make the repulsive force change smoothly and avoid deadlock due to the repulsive force balance at the target point. Finally, a communication mechanism between robots is introduced to realize real-time sharing of robot position information, so that a repulsive potential field of dynamic obstacles can be constructed for the neighboring robots. Experiments on simulation platform and robot platform verify the effectiveness of the method.
Batch-subsample Prototype Network Classification Model for Imbalanced and Overlapping Data

The classification of imbalanced and overlapping data is more difficult. When the number of categories is uneven and category areas are overlap, general neural network classifiers can only extract limited features from the data, which makes the model relies too much on existing samples, resulting in a low classification accuracy. Aiming at the above classification problem, this paper proposes a batch-subsample prototype network model. Based on the prototype network, the model achieves the separation of different categories in classification. In the training process, selecting each batch of training data by subsample method, so that the data for training is balanced each time, which effectively reduce the impact of category imbalance and overlapping on model training. This model is tested in the dataset NSL-KDD with the characteristics of imbalance and overlapping. The experimental results show that the classification accuracy of the batch-subsample prototype network (BSPN) model has been effectively improved, and the classification ability of minority categories also be better. To a certain extent, the model solves the problem that the neural network model has poor classification effect on imbalanced and overlapping data. Above all, the batch-subsample prototype network model can improve the classification accuracy in imbalanced and overlapping data, optimize the recognition rate of minority categories, and increase the classification robustness of the model, which is of great significance for imbalanced and overlapping data classification.
Improved YOLOv5 network method for remote sensing image objects detection

Aiming at the problems of missed detection and poor detection due to variable scale and complex background of remote sensing images, this paper proposes a network to enhance residuals and attention based on YOLOv5 model, named ERA-YOLO. The framework focuses on strengthening the feature representation capability of the backbone network. First, a Res2block module is designed in conjunction with the idea of res2net network structure to enable the network to represent multi-scale features at a finer level of granularity. Then, a coordinate attention module is inserted in the improved backbone network for improving the model's power to locate targets while suppressing the interference of background information. Experiments on a number of publicly accessible datasets demonstrate that the average detection accuracy is effectively improved compared to the original YOLOv5 and existing typical target detection models.
Efficient object detection of non-perishable in smart garbage sorting booths using the Efficient-Net model

For object detection in garbage sorting applications, the YOLOv4 model have been employed in the detection of abnormal objects during waste sorting. YOLO models have been proven as an effective solution with stable performance and accuracy. In the deployment, YOLO models usually consume considerable computational resources of GPU, which limit wide application of YOLO-based monitoring system. In addition, mobile terminals such as smart cameras have limited computing resources, and conventional AI models face difficulty in deployment on mobile terminals. In this case, it is of high practical significance and application value to carry out the research on the lightweight of the recognition model. The key point to realize the lightweight of the object recognition model without loss of recognition accuracy. This study designs and implements a lightweight object detection model based on the EfficientNet architecture, which can identify non-organic wastes in the perishable buckets of intelligent garbage sorting booths, thus performing monitoring of residents’ inappropriate garbage delivery behavior.
A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem

A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multiUAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.
Dynamic obstacle avoidance scheme based on model building and dimension reduction

Aiming at the current obstacle avoidance strategy for dynamic obstacle avoidance is difficult to achieve both accuracy and speed. Opencv library is used to process the depth images obtained by Intel D435i in combination with the attitude and position information of Intel T265, and the future movement trend of obstacles is obtained. Obstacle information and car information are summarized into a four-dimensional data and compressed into binary images. The improved A* algorithm is used to obtain the binary image and achieve excellent control effect. The speed and accuracy of dynamic obstacle avoidance are achieved.
A DCGAN image generation algorithm based on AE feature extraction

Current image generation algorithms have problems such as excessive noise in the generated images, poor image clarity and slow convergence of the generation network. To address the limitations of traditional image generation methods, we propose an image generation algorithm by combining an autoencoder (AE) and a deep convolutional generative adversarial network (DCGAN).The method first uses the feature representation capability of the AE to encode the training images, with the aim of compressing and downscaling the image to obtain an efficient data feature representation of the image. This has significantly less dimension than the original image and carries important features of the original image. This feature representation is then combined with random noise and used as the input to the DCGAN network after data processing. In the recognition phase, the network is continuously adjusted to optimize the model by using gradient descent and receives feedback from the discriminator. Experiments show that the network can generate images with better results to the extent that the human eye cannot separate them from the real data. The advantages of the proposed method are highlighted by comparing it with traditional generative networks.
Dual-stream multidimensional network for aluminum electrolysis process superheat identification

Superheat identification is very important in aluminum electrolysis industry. In order to realize the real-time identification of superheat, this paper proposes a novel dual-stream multidimensional network (DM) model, which is composed of an efficient stream and an accuracy stream. In efficient stream, a mobilenet is equipped on Artificial Intelligence of Things (AIoT) devices to achieve quick superheat identification as normality or abnormality. In accuracy stream, a two-stream information sharing 3D residual network is equipped on a cloud computing service to achieve accurate identification through abnormal frames. Experiments are conducted on the aluminum electrolysis datasets, and the results verify that our DM model can realize efficient and accurate identification under resource saving condition.
Path Planning Algorithm for Mobile Robots based on A* Algorithm with Adaptive Search Strategy

Path planning is one of the core functions of autonomous navigation. A* algorithm is widely used in the study of path planning for robots. Traditional A* algorithm is limited by the direction of node expansion. In some cases, it would generate some unnecessary bends, which makes the path not suit the motion characteristics of the robot and results in bad affection on its path. An improved A* algorithm with search strategy is proposed. The strategy of neighborhood point searching for A* algorithm is presented, so that A* algorithm has a more reasonable node search direction in a wider area. To further solve the unfavorable bending problem, the heuristic function takes the turns of the path into account in the cost function, which can reduce certain types of path turning that affect the robot’s motion. Finally, the improved A* algorithm is verified under simulation environment. Comparison tests with traditional A* algorithm, the path length of the proposed algorithm is reduced by 13.0 %, and the number of turns is reduced by 42.9 %. Compared with Dijkstra algorithm, the total time cost of the proposed algorithm is shortened by 12.3 %.
A Semantic Interpretation Method for Deep Neural Networks Based on Knowledge Graphs

Despite the great success of deep neural networks in many fields, the lack of interpretability has severely limited their wide application in security-sensitive tasks. Although current interpretable methods of deep neural networks such as visualization, class activation mapping, and sensitivity analysis can help users intuitively understand the inner working mechanism of the neural networks to some extent, they are either too coarse in explanation or too complex in explanation form to read easily. In order to use semantic information which is more understandable and close to human thought to interpret the deep neural network and increase the readability of the interpretation,we propose a semantic interpretation method of deep neural network based on knowledge graph. The method takes the VGG16 network as an example, and by mining the key neuronsof the neural network, construct semantic dictionaries and knowledge maps of key neurons, and automatically generate human-understandable semantic explanatory statements based on the knowledge maps. The method provides a new idea to improve the transparency of the operation process of deep neural networks, and also provides a clearer reference basis for pruning and tuning of deep neural networks.
Support Vector Machine Based on Orthogonal Efficient Hinging Hyperplanes Neural Network

In this paper, we present a support vector machine classifier model based on an efficient hinging hyperplanes neural network, in order to solve the classification problem. Firstly, an efficient hinging hyperplanes network is established. The hidden layer neuron connection of the network can be regarded a directed acyclic graph. Secondly, the unique neuron generation method of the network ensures the independence of piecewise linear mapping of input features. Selecting the output of the target neuron as the kernel function of the support vector machine, the support vector machine based on efficient hinging hyperplanes is constructed. Finally, the support vector machine based on efficient hinging hyperplanes is compared with other prevailing nonlinear classifiers. It can be concluded that the support vector machine based on efficient hinging hyperplanes network can complete the classification task efficiently and ensure faster classification speed and higher classification accuracy.
Sequential fusion state estimation algorithm for multi-sensor nonlinear systems with cross-correlated noise

This paper is concerned the problem of multi-sensor state estimation with cross-correlated noise, this paper adopts sequential fusion to estimate the state. The statistical characteristics of measurement noise of different sensors in the multi-sensor system is related, and also related to the system noise in one step. Firstly, based on the estimation of observation noise, a global optimal fusion filter based on sequential fusion and Cubature Kalman filter is proposed for the first time. Secondly, the algorithm proposed in this paper is simulated by numerical method. The method of numerical realization is Cubature Kalman filter based on deterministic sampling. Finally, the effectiveness of the proposed algorithm is demonstrated by a simulation example.
Cross Guided and Pyramid Aggregation Networks for Real-time Semantic Segmentation

Recent advances have achieved a significant leap forward in image semantic segmentation with deep neural networks. Still, high precision tends to rely on rich contextual information and high-resolution spatial detail, both of which incur high computational costs. Balancing high accuracy with high efficiency is a challenging problem, particularly in road scenes requiring low latency. This research employs a lightweight model for real-time semantic segmentation, which we refer to as CPANet. Specifically, for multi-scale information extraction of low-resolution features, we use the Deep Aggregation Pyramid Pooling Module (DAPPM) cascade structure to enrich the feature representation. In addition, to further enhance the efficiency, we gradually reduced the number of channels in the decoding process and utilized Cross Guided Module (CGM) for different aggregate levels of feature information. Finally, we use an auxiliary training strategy in the training phase, which enables us to enhance the network’s performance without additional inference overhead. Extensive evaluations have shown that our method performs superior in accuracy when compared to other methods. On the Cityscapes test set, the proposed models have achieved 72.5% mIoU/234.5 FPS and 78.0% mIoU/84.55 FPS on NVIDIA GTX 1080Ti.
SS-RRT*: A Safe and Smoothing Path Planner for Mobile Robot in Static Environment

The safety and energy loss are the fundamental issues of robot planning, which can achieve its practicability in real world applications. In this paper, a method which is based on a hybrid strategy is proposed. The main framework is described as follows: firstly, a map that expresses the environmental information is constructed based on Quick-RRT*. A hybrid strategy that guarantees node and path safety is proposed. Unsafe nodes are deleted from the sampled nodes in the constructed map, and a security path is generated. Furthermore, an improved Bezier curve algorithm is proposed to smooth the planned path. The experimental results show that the proposed method can generate an asymptotically optimal path and has better convergence performance than related works.
FPGA-based Acceleration of Structured Light Depth Estimation

Stereo depth estimation is widely applied in computer vision and intelligent robotic systems. For real-time mobile stereo vision applications, how to efficiently design and implement stereo matching on resource-limited hardware platforms is still a grand challenge. In this paper, we propose and implement a low-power, high-resolution stereo matching hardware architecture on field-programmable gate array (FPGA) with the input of structured light camera. In order to obtain high-quality point cloud data, we present an image preprocessing module and an improved cost function for stereo matching algorithm. Furthermore, we leverage hardware description language to implement our stereo matching computing architecture and all algorithm models are evaluated on the Xilinx ZCU102 development board, which only takes about 61147 LUTs and 786 BRAMs. Compared with high-performance CPUs, the computing speed is significantly improved, 1.84 times faster than Intel i7-10700@2.90GHz, and the power consumption is only 3.915W.
Vibration displacement extraction method based on SVD decomposition and wavelet denoising

To address the problem of noise signal interference in singular value vibration signal extraction methods, this paper proposes a vibration extraction algorithm combining singular value decomposition and improved wavelet denoising. Firstly, the singular value decomposition method is used to extract the useful information in the high-speed video sequence, and the singular values and singular vectors associated with the vibration information are selected by judgement. Then the vibration signal is denoised using an improved wavelet denoising function. Through comparison experiments, the algorithm proposed in this paper can effectively reduce the noise in vibration signals and has some prospects in vibration signal processing applications.
Application of Model Compression Technology Based on Knowledge Distillation in Convolutional Neural Network Lightweight

Nowadays, the neural networks become deeper and deeper, the number of parameters has continued to increase and the complexity of model has also continued to increase. These models are difficult to deploy in embedded devices, consequently the lightweight of deep neural networks is particularly urgent. To solve these problems, knowledge distillation comes into being. Through knowledge distillation, the model can be simplified. This paper discusses knowledge distillation, a relatively flexible and efficient model compression method, describes how the deep neural networks developed and the development of knowledge distillation technology, and then introduces the method and principle of knowledge distillation in detail. We accomplished experiments on the CIFAR-10 dataset and the distillation results at different temperatures were obtained to achieve the certain results of knowledge distillation. Finally, the future development of this technical method was prospected.