Scene 3-D Reconstruction System in Scattering Medium

Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and extensions.The NeRF(Neural Radiance Fields)algorithm,suitable for underwater scenes or scattering media,is also evolving.Existing underwater 3D reconstruction systems still face challenges such as long training times and low rendering efficiency.This paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D reconstruction.First,we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium and ensure consistency in enhancement across frames.Then,we perform keyframe selection to optimize resource usage and reduce the impact of dynamic objects on the reconstruction results.After pose estimation using COLMAP,the selected keyframes undergo 3D reconstruction using neural radiance fields(NeRF)based on multi-resolution hash encoding for model construction and rendering.In terms of image enhancement,our method has been optimized in certain scenarios,demonstrating effectiveness in image enhancement and better continuity between consecutive frames of the same data.In terms of 3D reconstruction,our method achieved a peak signal-to-noise ratio(PSNR)of 18.40 dB and a structural similarity(SSIM)of 0.6677,indicating a good balance between operational efficiency and reconstruction quality.

YOLOv5ST:A Lightweight and Fast Scene Text Detector

Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

A Dual Domain Robust Reversible Watermarking Algorithm for Frame Grouping Videos Using Scene Smoothness

The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos.Grounded in the H.264 video coding standard,the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel.Subsequently,it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information,enabling successful watermark extraction and lossless recovery of the original video content.Experimental results demonstrate the algorithm’s strong imperceptibility,with each embedded frame in the experimental videos achieving a mean peak signal-to-noise ratio of 49.3830 dB and a mean structural similarity of 0.9996.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 7.59%and 0.4%on average.At the same time,the proposed algorithm has strong robustness to both offline and online attacks:In the face of offline attacks,the average normalized correlation coefficient between the extracted watermark and the original watermark is 0.9989,and the average bit error rate is 0.0089.In the face of online attacks,the normalized correlation coefficient between the extracted watermark and the original watermark is 0.8840,and the mean bit error rate is 0.2269.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 1.27%and 18.16%on average,highlighting the algorithm’s robustness.Furthermore,the algorithm exhibits low computational complexity,with the mean encoding and the mean decoding time differentials during experimental video processing being 3.934 and 2.273 s,respectively,underscoring its practical utility.

Intelligent Sensing and Control of Road Construction Robot Scenes Based on Road Construction

Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.

Autonomous landing scene recognition based on transfer learning for drones

In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network(CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum(ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent(SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.845 0% top-1 accuracy on the LandingScenes-7dataset, paving the way for drones to autonomously learn landing scenes.

Traffic Scene Captioning with Multi-Stage Feature Enhancement

Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images,ensuring road safety while providing an important decision-making function for sustainable transportation.In order to provide a comprehensive and reasonable description of complex traffic scenes,a traffic scene semantic captioningmodel withmulti-stage feature enhancement is proposed in this paper.In general,the model follows an encoder-decoder structure.First,multilevel granularity visual features are used for feature enhancement during the encoding process,which enables the model to learn more detailed content in the traffic scene image.Second,the scene knowledge graph is applied to the decoding process,and the semantic features provided by the scene knowledge graph are used to enhance the features learned by the decoder again,so that themodel can learn the attributes of objects in the traffic scene and the relationships between objects to generate more reasonable captions.This paper reports extensive experiments on the challenging MS-COCO dataset,evaluated by five standard automatic evaluation metrics,and the results show that the proposed model has improved significantly in all metrics compared with the state-of-the-art methods,especially achieving a score of 129.0 on the CIDEr-D evaluation metric,which also indicates that the proposed model can effectively provide a more reasonable and comprehensive description of the traffic scene.

Object detection in crowded scenes via joint prediction

Detecting highly-overlapped objects in crowded scenes remains a challenging problem,especially for one-stage detector.In this paper,we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme,named YOLO-CS.Specifically,we give YOLOv4 the power to detect multiple objects in one cell.Center to our method is the carefully designed joint prediction scheme,which is executed through an assignment of bounding boxes and a joint loss.Equipped with the derived joint-object augmentation(DJA),refined regression loss(RL)and Score-NMS(SN),YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time.Furthermore,on the widely used general benchmark COCO,YOLOCS still has a good performance,indicating its robustness to various scenes.

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

Multi-Modal Scene Matching Location Algorithm Based on M2Det

In recent years,many visual positioning algorithms have been proposed based on computer vision and they have achieved good results.However,these algorithms have a single function,cannot perceive the environment,and have poor versatility,and there is a certain mismatch phenomenon,which affects the positioning accuracy.Therefore,this paper proposes a location algorithm that combines a target recognition algorithm with a depth feature matching algorithm to solve the problem of unmanned aerial vehicle(UAV)environment perception and multi-modal image-matching fusion location.This algorithm was based on the single-shot object detector based on multi-level feature pyramid network(M2Det)algorithm and replaced the original visual geometry group(VGG)feature extraction network with the ResNet-101 network to improve the feature extraction capability of the network model.By introducing a depth feature matching algorithm,the algorithm shares neural network weights and realizes the design of UAV target recognition and a multi-modal image-matching fusion positioning algorithm.When the reference image and the real-time image were mismatched,the dynamic adaptive proportional constraint and the random sample consensus consistency algorithm(DAPC-RANSAC)were used to optimize the matching results to improve the correct matching efficiency of the target.Using the multi-modal registration data set,the proposed algorithm was compared and analyzed to verify its superiority and feasibility.The results show that the algorithm proposed in this paper can effectively deal with the matching between multi-modal images(visible image–infrared image,infrared image–satellite image,visible image–satellite image),and the contrast,scale,brightness,ambiguity deformation,and other changes had good stability and robustness.Finally,the effectiveness and practicability of the algorithm proposed in this paper were verified in an aerial test scene of an S1000 sixrotor UAV.

A Lightweight Road Scene Semantic Segmentation Algorithm

In recent years,with the continuous deepening of smart city construction,there have been significant changes and improvements in the field of intelligent transportation.The semantic segmentation of road scenes has important practical significance in the fields of automatic driving,transportation planning,and intelligent transportation systems.However,the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation edges.Therefore,this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these issues.The model uses the lightweight backbone network MobileNet instead of the LiteSeg backbone network to reduce the network parameters and computation,and combines the Coordinate Attention(CA)mechanism to help the network capture long-distance dependencies.At the same time,by combining the dependencies of spatial information and channel information,the Spatial and Channel Network(SCNet)attention mechanism is proposed to improve the feature extraction ability of the model.Finally,a multiscale transposed attention encoding(MTAE)module was proposed to obtain features of different resolutions and perform feature fusion.In this paper,the proposed model is verified on the Cityscapes dataset.The experimental results show that the addition of SCNet and MTAE modules increases the mean Intersection over Union(mIoU)of the original LiteSeg model by 4.69%.On this basis,the backbone network is replaced with MobileNet,and the CA model is added at the same time.At the cost of increasing the minimum model parameters and computing costs,the mIoU of the original LiteSeg model is increased by 2.46%.This article also compares the proposed model with some current lightweight semantic segmentation models,and experiments show that the comprehensive performance of the proposed model is the best,especially in achieving excellent results in small object segmentation.Finally,this article will conduct generalization testing on the KITTI dataset for the proposed model,and the experimental results show that the proposed algorithm has a certain degree of generalization.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.

Exploiting Human Pose and Scene Information for Interaction Detection

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image frame.The third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

Scene image recognition with knowledge transfer for drone navigation

In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

Development Strategy of Urban Cultural Scene to Promote the Upgrading of Regional Cultural Consumption in Shaanxi

Regional cultural patterns and characteristics play a positive role in economic and social development.By planning and constructing cultural amenities and creating cultural scenes,the spatial quality and quality of life in a region can be enhanced,facilitating the expansion of cultural consumption.Shaanxi,with its rich historical and cultural resources,positions the capital city of Xi’an as a“world historical city”,boasting a vast number of cultural amenities represented by“cultural facilities”,“cultural activities”,“cultural experiences”,and“cultural services”.The development of urban cultural scene,with the aim of promoting the upgrading of regional cultural consumption in Shaanxi,requires comprehensive planning and a multifaceted approach,particularly in integrating provincial cultural scenes,clarifying the positioning of cultural scenes,innovating cultural scene experience projects,creating cultural scene intellectual property(IP),and empowering cultural scenes through the application of science and technology.

Analysis of the Writing Characteristics of the Title Music,Using“Scenes of Childhood”and“Children’s Garden”as Examples

In European thought and culture,there exists a group of passionate artists who are fascinated by the intention,passion,and richness of artistic expression.They strive to establish connections between different art forms.Musicians not only attempt to represent masterpieces through the language of music but also aim to convey subjective experiences of emotions and personal imagination to listeners by adding titles to their musical works.This study examines two pieces,“Scenes of Childhood”and“Children’s Garden”,and analyzes the different approaches employed by the composers in portraying similar content.

多场景下基于传感器的行为识别

针对基于传感器的行为识别任务中识别场景单一且固定的问题,提出一种多场景下基于传感器的行为识别迁移模型,由基于传感器的动态感知算法(dynamic perception algorithm,DPA)和自适应场景的行为识别迁移方法(adaptive scene human recognition,AHR)两部分组成,解决在固定场景下对传感器的依赖性以及在场景转换时识别模型失效的问题。DPA提出两阶段迁移模式,将行为识别阶段和模型迁移阶段同步推进,保证模型在传感器异动发生后仍能持续拥有识别能力。进一步提出AHR场景迁移方法,实现模型在多场景下的行为识别能力。实验验证该模型具有更优的适应性和可扩展性。

深度学习的自然场景文本识别方法综述

自然场景文本识别在学术研究和实际应用中具有重要价值,已经成为计算机视觉领域的研究热点之一。然而,识别过程存在文本风格多样、背景环境复杂等挑战,导致识别效率和准确率不佳。传统的基于手工设计特征文本识别方法由于其有限的表示能力,不足以有效地应对复杂的自然场景文本识别任务。近年来,采用深度学习方法在自然场景文本识别中取得了重大进展,系统地梳理了近年来相关研究工作。首先,根据是否需要对单字符进行分割,将自然场景文本识别方法分为基于分割与无需分割的方法,再根据其技术实现特点将无需分割的方法进行细分,并对各类最具有代表性的方法工作原理进行了阐述。然后,介绍了当前常用数据集以及评价指标,并在数据集上对各类方法进行了性能对比,从多个方面讨论了各类方法的优势与局限性。最后,指出基于深度学习的自然场景文本识别研究存在的不足和难点,对其未来的发展趋势进行了展望。

虚拟现实场景下的精准化采摘机器人作业研究

利用虚拟现实场景仿真技术,对葡萄采摘机器人的精准作业控制过程进行分析,通过构建虚拟现实环境下葡萄采摘机器人作业场景模型,以机器人采摘运动柔顺、无碰撞和不损伤采摘对象为目标,对采摘作业过程的夹紧、托举以及剪切动作进行虚拟仿真。虚拟现实场景下20次采摘动作仿真结果中,有3次采摘发生碰撞、17次采摘成功。这表明,在设计阶段利用虚拟现实场景进行精准化作业仿真和控制算法测试,能够有效缩短采摘机器人研制过程中的调试和优化时间。

动态场景的三维重建研究综述

随着静态场景三维重建算法的不断成熟,动态场景三维重建算法成为近年来的研究热点和研究难点。现有的静态场景三维重建算法对静止的对象有较好的重建效果,一旦场景中对象出现变形或者是相对运动,其重建效果不太理想,因此发展对动态场景的三维重建研究工作是相当重要的。简要介绍三维重建的相关概念及基本知识、静态场景三维重建和动态场景三维重建的研究分类及研究现状;全面总结了动态场景三维重建研究最新进展,将动态场景三维重建按照基于RGB数据源的动态三维重建和基于RGB-D数据源的动态三维重建进行分类,其中RGB数据源下又可划分为基于模板的动态三维重建、基于非刚性运动恢复结构的动态三维重建和RGB数据源下基于学习的动态三维重建,RGB-D数据源下主要总结归纳基于学习的动态三维重建,对各类典型重建算法进行了介绍和对比分析;介绍了动态场景三维重建在医学、智能制造、虚拟现实与增强现实、交通等领域的应用;提出了动态场景三维重建的未来研究方向,并对这个快速发展领域中的各个方向研究进行了展望。