Reviewerly/exHarmony huggingface dataset:
Multi-UAVs Targets Search Based on Distributed Cooperative Heuristic Algorithm

In recent years, multi-agents cooperative algorithm has received extensive attention, and gradually applied to various agents such as unmanned aerial vehicle (UAV). This paper proposes a distributed cooperative heuristic (DCH) algorithm for the mission of multi-UAVs search multi-targets, aiming at the problem of cooperative search path planning for multi-UAVs in an environment with obstacles. The proposed method DCH is based on receding horizon optimization (RHO) strategy and cooperation-competition scheme. According to the change of environment, the prediction time-domain of RHO is changed, and RHO with variable step-ahead prediction is introduced. Moreover, the pigeons-inspired cooperation-competition (PICC) scheme for multi-UAVs is designed, including cooperation module and competition module. Through information interaction, multi-UAVs can expand their search horizons, and cooperate with each other to improve search efficiency. In addition, the environmental cognitive information map (ECIM) and value income function (VIF) is also reasonably improved. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed algorithm uses less time and shows a better performance of target search.
Multi-scale Traffic Sign Detection and Recognition Method Fused with Attention Mechanism

For the problems of the traditional methods in the process of small target detection of traffic signs, such as being susceptible to environmental factors, poor real-time performance and weak generalization ability, this paper proposes an improved road traffic sign detection and recognition method based on YOLOv5 target detection model. Firstly, the original target box in YOLOv5 network which was not suitable for this detection task is improved, and the clustering method of target box obtained from the original network was optimized to K-Means++ clustering method to generate new anchor coordinates. Secondly, the CSP structure of YOLOv5 network was replaced by the CSPGhost structure composed of Ghost module. The CBAM attention mechanism module is added behind the backbone network to improve the image feature extraction ability of the network model. Finally, the detection layer structure of the model is improved, and the detection scale of the target is increased to improve the detection accuracy of the model for small targets. In order to verify the effectiveness of the improved network, a model comparison experiment is designed in this paper. It shows that 45 types of traffic signs are detected on TT100K traffic sign dataset. The experimental results show that the accuracy of the improved network is improved by 7.26% compared with YOLOv5 network.
Application of Soft Actor-Critic Reinforcement Learning to a Search and Rescue Task for Humanoid Robots

This paper proposes a novel maximum entropy based reinforcement learning for dealing with a robotic search and rescue task in a complex enclosed environment. The search and rescue task is described as a Markov Decision Process, under which an auxiliary reward function at multiple stages is designed for the robot and its interaction with the specified environment. A variant of the state-of-art reinforcement learning algorithm, goal-based Soft Actor-Critic (SAC), is developed to train a humanoid robot. Simulation results verify the effectiveness of the proposed goal-based SAC algorithm and its advantages comparing with the prototype of SAC algorithm for the same task.
Light Field Depth Estimation Based on Channel Attention and Edge Guidance

Light field images incorporate the angular and spatial distortion of the captured light. Benefiting from the abundant information contained in the light field, depth estimation emerges as one of its key applications. In this article, we presented a neural network in order to precisely estimate the depth of the light field. The proposed network fully considers the rich feature information of the full light field image, designs an attention mechanism to select more valuable views in the same channel branch and performs multi-feature fusion to enhance the inference accuracy of the occluded region. Meanwhile, the network extracts the edge detail features of the target for guidance to provide more effective information for depth estimation. The proposed network achieves positive results and performance on the commonly used 4D HCI dataset in quantitative, qualitative, and computational cost evaluations.
Combing Transformer and CNN for Monocular Depth Estimation

The goal of this study is to explore the monocular depth estimation problem.. Since visual transformer performs exceptionally well in modeling long-range correlation, we suggest modeling the global context using the Swin Transformer for accurate depth estimation. Besides, we create a CNN branch to assist the network in gathering local information. The ablation experiments verify the effectiveness of CNN branches in capturing local features. Experimental outcomes on the NYUv2 dataset reveal that our method achieves better performance than existing state-of-the-art monocular depth estimation methods.
An Unsupervised Anomaly Detection Scheme based on Memory Mechanism in Cloud-edge Collaboration Scenario

In order to timely reject defective products in high-speed assembly lines, the task of anomaly detection is widely used in industrial production process. Recently, unsupervised anomaly detection has made tremendous developments as more scholars have started to focus on this field. The model detects abnormal samples whose data distribution deviate from the normal data distribution. However, due to the problem of data island in machine learning, it is difficult to achieve global optimization for models trained on local datasets. In this work, a memory network based algorithm for cloud-edge collaborative anomaly detection is proposed to break down data barrier. Our proposed method obtains an anomaly detection model using local datasets trained at each edge side, and the local model updates the parameters in the cloud to obtain the global model, which decoupling the data retrieval and the training of the global model. In addition, due to the inconsistent data distribution, we design the memory module to retain the data features from each edge in order to further improve the detection capability of the global model. The memory module from each edge is iteratively updated in the cloud with the training of the model, so that our final global anomaly detection model retains the information about data distribution features from different domains of different edges.
FPGA Accelerated Real-time Recurrent All-Pairs Field Transforms for Optical Flow

Optical flow algorithms based on deep learning have achieved excellent performance on multiple datasets, bringing new opportunity for optical flow estimation. Recurrent All-Pairs Field Transforms (RAFT) is one of the most powerful deep network based optical flow algorithms, but it is difficult to process in real time on the resource-limited embedded platform. In this paper, we propose RAFT-Lite by compressing the original RAFT model, which is more lightweight and suitable for hardware deployment. We further propose a hardware accelerating architecture on FPGA for RAFT-Lite, which provides an efficient scheduling strategy for the convolution in RAFT to achieve efficient pipeline and resource reuse. On Xilinx ZCU102 evaluation board, the accelerated hardware system can reach 10.4fps processing images with a resolution of 512*396, which is 6.8x of i7-10700@2.90GHz and 46x of ARM Cortex-A53@1.50GHz. Besides, the power consumption is 13.103W.
Switching GMM-HMM for Complex Human Activity Modeling and Recognition

Complex human activities can be decomposed into primitive activities (PAs) that happen sequentially but may vary in order or frequency among different observation sequences. The Gaussian mixture model based hidden Markov model (GMM-HMM) is widely used for activity modeling but is only capable of modeling activities with fixed pattern trajectories. To overcome the drawback of GMM-HMM, a hierarchical structure is introduced to form the Switching Gaussian mixture model based hidden Markov model (S-GMMHMM), where PAs are treated as upper layer states and the continuous observation sequence emitted by each PA is modeled by a GMM-HMM. To ensure that the upper layer states correspond to real activities, a supervised algorithm is proposed for S-GMMHMM parameter estimation. For the purpose of less time complexity, a real-time activity recognition algorithm is proposed by computing activity posteriors recursively. Experiment results show that the proposed model outperforms GMM-HMM in activity recognition, while brings a notable reduction in time complexity.
Dynamic Path Planning of USV Based on Improved Artificial Potential Field Method in Harsh Environment

To overcome the shortcomings of traditional artificial potential field method in unmanned surface vehicle(USV) path planning, a waypoint generation algorithm considering the USV’s gyration radius constraint is proposed, which can make the planned path smoother and more consistent with the motion characteristics of the USV. The additional lateral force algorithm is designed to detect possible local minimum points and guide the USV out of them in advance. By establishing the obstacle detection mechanism, the redundant cornering angle and path length are reduced when the USV passes through the obstacles. A multi-dimensional path evaluation function is established, which can be used to evaluate the planned paths reasonably. Simulation results show that better path planning for USV in complex environment can be achieved by this new method.
Swin Transformer based Unsupervised Network for Low-Light Image Enhancement

Images captured under poor illumination suffer from poor visibility and weak information. The majority of low-light image enhancement (LLIE) methods are based on convolutional neural networks (CNNs), which leads to their inability to establish long-range context interaction. In this paper, we take the advantages of the Swin Transformer and ResNet to develop the Swin Transformer-based unsupervised Generative Adversarial Network (STUN) for the LLIE task. The STUN contains one generator and one discriminator. The generator includes modules for feature extraction, deep feature processing and image reconstruction. In particular, we use Swin Transformer blocks and ResNet in the deep feature processing module alternately to calculate the global and local attention. The self-feature preserving loss and the spatial consistency loss are employed to constrain the unsupervised learning of STUN. Experimental results on several low-light datasets indicate that STUN can achieve great performance in both visual quality and evaluation metrics.
Improved Lightweight Helmet Wearing Detection Method For YOLOXs

Aimed at the problems of slow check speed and low accuracy in the existing safety helmet wearing detection methods, an improved YOLOXs helemt wearing detection method was proposed. First, to be able to make the model’s overall construction lightweight and its size smaller, the YOLOXs backbone feature extraction network CSPdarknet is replaced with MobileNetV2, and the basic convolution procedure is the depthwise separable convolution. Secondly, in order to enrich the network feature information and make full use of the available network capacity, a lightweight ECA attention mechanism is added to the inverted residual structure of MobileNetV2. Finally, through knowledge distillation, the model is fine-tuned to obtain Mbv2E-YOLOXs. The experimental results on the public data set SHWD shows that compared to YOLOXs algorithm, the improved algorithm reduces the amount of model parameters and model calculation by 13% and 18.9% respectively; the average accuracy, safety helmet detection accuracy, and worker head detection accuracy increased by 7.6%, 9.25%, and 5.8% respectively, and the detection speed of the model reaches 60frame/s. Therefore, the improved YOLOXs model has good real-time performance and better accuracy for helmet wearing detection, which can meet the detection needs of practical application scenarios.
Ensemble Clustering Based on Manifold Broad Learning System

Ensemble clustering has made significant progress in improving clustering performance by integrating multiple basic partitions. However, the conventional ensemble clustering methods rarely combine the rich information from both basic partitions and feature space comprehensively and efficiently. Facing the limitation, we design an Ensemble Clustering Based on Manifold Broad Learning System (EC-MBLS) in this paper. EC-MBLS firstly constructs a fuzzy partition matrix to capture the cluster-wise similarities from basic partitions. Then a manifold broad learning system (MBLS) is designed to learn a better consensus partition, which is guided by the global cluster structure of data and constrained by the local affinity of data. An efficient solution is finally provided to optimize the MBLS. Compared with the state-of-the-art ensemble clustering methods, experiments on multiple real-world clustering tasks are carried out to demonstrate the effectiveness of EC-MBLS.
Vehicle object detection algorithm with SSD fused RFP structure

Object detection is extensively used in sectors as intelligent transportation and monitoring, however the current general single shot multibox detector (SSD) has the drawback of being unable to detect small objects. This study proposes an improved SSD to enhance the detection accuracy of small objects, dubbed RFP-SSD. It adopts Resnet50 as the backbone network and fuses an attention mechanism to boost shallow feature extraction in the feature extraction stage. Further, a recursive feature pyramid (RFP) structure is applied to facilitate multiscale fusion among feature maps. The experimental findings reveal that RFP-SSD outperforms the SSD in terms of detection. The mAP of RFP-SSD is 87.77%, which is a 6.77% improvement over the general SSD.
Lightweight Object Detection Based on Super-resolution

Deep learning-based object detection has improved detection accuracy compared with traditional object detection. But there are problems such as large network parameters and high computer hardware requirements, making it difficult to meet the deployment on embedded or mobile devices. To address this, we optimize the classical SSD algorithm and propose a lightweight SSD object detection algorithm with super-resolution feature fusion. First, MobileNetv2 is used instead of VGG-16 as the backbone network. Secondly, five additional layers are added to generate feature maps of different sizes using the improved MixConv algorithm. Finally, the designed super-resolution feature fusion module generates a new feature pyramid. The test results on the PASCAL VOC dataset show that the model parameters are reduced by 76.26%, and the model complexity (FLOPs) is reduced by 83.13% compared to the original SSD algorithm. The detection speed increases by 1.27 times to 58.3 frames per second, and the average detection accuracy (mAP) can reach 72.7%. While ensuring accuracy, it reduces the requirement of the network on computer hardware and realizes the lightweight of the network.
Research on Path Planning of Mobile Robot Based on Reinforcement Learning

With the continuous change of artificial intelligence technology, the study of path planning for mobile robots is no longer limited to traditional path algorithms. Reinforcement learning, as an artificial intelligence algorithm with excellent performance in the field of path planning, has also gradually become the object of research on path planning. In order to find a fast path from the starting point to the end point of a mobile robot in a complex environment, reinforcement learning is used to find a valid path by sensing the environment and continuously receiving rewarding feedback through a method of trial and error learning like humans. Therefore, this paper selects two algorithms to verify their effectiveness on the basis of building a two-dimensional grid map. The final experimental results show that both reinforcement learning algorithms can eventually avoid obstacles and plan a valid path in a complex environment through continuous learning iterations. Q-Learning also reduces the path distance by 10% and the number of convergence iterations by 82% compared to Sarsa.
Classified Collaborative Navigation Algorithm for UAV Swarm in Satellite-denied Environments

Collaborative navigation technology realizes positioning through the interactive sharing of various navigation information among individuals, which has outstanding effect on raising navigation accuracy. Aiming at the problem that it is difficult for unmanned aerial vehicle (UAV) to achieve long-time and high-precision navigation in satellite-denied environments, this paper puts forward a classified collaborative navigation algorithm, which can be used in satellite-denied environments for UAV swarm. Firstly, a real-time classification method of UAV swarm is designed based on the relative navigation state between the UAV and the ground base station at the current moment. Then, according to the inertial navigation system (INS) error characteristics and the relative navigation information, model the collaborative navigation system. Finally, the INS errors of UAV are estimated and corrected by Kalman filter. Simulation proves that the proposed collaborative navigation algorithm can effectively raise the navigation accuracy of UAV swarm in satellite-denied environment on the one hand, and enhance the reliability and robustness of the collaborative system on the other hand.
Multimodal Feature Fusion YOLOv5 for RGB-T Object Detection

Multimodal image pairs (e.g. visible and thermal images) can provide mutually beneficial pixel information and enhance the robustness and reliability of object detection in applications such as autonomous driving and video surveillance. To benefit from the effective information of both modalities, a multimodal feature fusion network based on YOLOv5 is proposed in this paper. Multimodal feature fusion adaptive weighting module is designed to perform feature extraction and fusion at three scales in the network to achieve the best utilization of multimodal features. Experiments show that our multimodal object detection network (MFF-YOLOv5) achieves better performance on two public datasets compared with the current state-of-the-art (SOTA) methods.
An improved RNA genetic algorithm without mutation operation in the later stage

In the late stage of the population evolution of the RNA genetic algorithm, mutation may destroy the individual genes with higher fitness, resulting in slower convergence or no optimal solution. Therefore, this paper proposes an RNA genetic algorithm that does not add mutation operations in the late stage of evolution (Referred to as nmlsRNA-GA), in the early stage of evolution, the goal is to find the approximate location of the optimal solution. The two-point crossover and fixed-probability mutation operations are performed in the crossover pool composed of individuals with high fitness and individuals with low fitness to increase the diversity of the population. In order to improve the global search ability of the algorithm. In the later stage of the evolution, the goal is to improve the local search ability to determine the optimal solution, and use individuals with high adaptability to cross over individuals with high adaptability to generate individuals with higher adaptability, thereby improving search Accuracy, no mutation operation is added at this stage to prevent mutation from destroying excellent genes. By optimizing five typical high-dimensional test functions, and comparing them with the RNA genetic algorithm that uses mutation throughout the process, the analysis results show that the algorithm has better performance.
A Learning-based Projected Gradient Descent Approach for Blind Super-resolution

Kernel estimation is an important component in the blind super resolution (BSR), and significantly dominates the performance of the restored image. Most of the state-of-the-art approaches, such as FKP-DIP, is proposed to formulate the kernel estimation as a cumbersome network-based end-to-end model, which provides promising performance through kernel prior pre-training behavior but lack of flexibility and generalization-ability towards different kernels in terms of scales, categories and blur level. Thus, this paper strives to reformulate the kernel estimation through a learning aided projected gradient descent (LPGD) algorithm, referring to LPGD-DIP. The LPGD algorithm iteratively optimizes the kernel with respect to the gradient based principle, therefore providing good generalization-ability for arbitrary kernel estimation. Meanwhile, a lightweight network is applied to dynamically and adaptively learn the update rule at each step, whereby the performance is also guaranteed through the learning-based network behavior. Numerical results show that the LPGD-DIP surpass the existing BSR approaches in the aspect of the kernel estimation, the image reconstruction in the most Gaussian blur scenarios with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LPGD-DIP.
Identification of High-speed Railway Trackside Equipments Based on YOLOv4

In view of the shortcomings of low detection accuracy and low efficiency in the traditional manual detection of trackside equipment by using simple measurement tools, this paper aims to obtain an efficient and fast detection method for identifying all trackside equipments through model training based on YOLOv4 target detection algorithm and taking the balise, telephone pole and telephone pole number on highspeed railway line as the research object. In the process, the identification method of multi-target trackside equipments is obtained through the steps of labeling the original data set, enhancing data set, obtaining the anchor box by improved k-means clustering, training and tuning YOLOv4 model, and the detection scheme is evaluated by comparison. The results show that after improving the anchor box parameters, the model accuracy improved by 7.23%, the value can reach 84.83%, the video detection speed is also increased by 7.58%, and the detection performance is significantly improved.
A Music Performance Method Based on Visual Gesture Recognition

This paper proposes a music performance method based on visual gesture recognition. The method uses user-defined gestures to control musical parameters such as pitch name, octave, accidentals, instrument, pitch bend, note value, and frequency equalizer. Next, the method uses computer vision algorithms to recognize different gestures in the webcam real-timely to achieve the purpose of music performance. In terms of dynamic gesture recognition, the offline recognition accuracy of the action classifier is 98.85%, and the real-time accuracy is 90.8%. Finally, based on this method, this paper implements a novel musical instrument based on visual gesture recognition. Compared with other musical instruments, the instrument has the advantages of convenient deployment and easy operation.