Nuclear Track Detectors for Relativistic Nuclear Fragmentation Studies: Comparison with Other Competitive Techniques

The potential of the high resolution nuclear track detector (NTD) CR-39 is examined carefully for the measurement of relativistic nuclear projectile fragmentation cross sections and studies of related processes using the experience of many years of such measurements. The charge resolution and the charge resolving power of CR-39 detectors for the measurements of 158 A GeV 207Pb projectiles and their fragments are presented. Exposures of target-detector stacks, the chemical etching procedure and the nuclear track measurements are described in detail discussing precautions and possible errors. The procedures discussed are also valid for other NTDs. A comparison with electronic active detectors is also made considering important detection and measurement aspects. An experimental design proposing the co-use of NTDs with in-use active detectors is described.

Robust Space-Time Adaptive Track-Before-Detect Algorithm Based on Persymmetry and Symmetric Spectrum

Underwater monopulse space-time adaptive track-before-detect method,which combines space-time adaptive detector(STAD)and the track-before-detect algorithm based on dynamic programming(DP-TBD),denoted as STAD-DP-TBD,can effectively detect low-speed weak targets.However,due to the complexity and variability of the underwater environment,it is difficult to obtain sufficient secondary data,resulting in a serious decline in the detection and tracking performance,and leading to poor robustness of the algorithm.In this paper,based on the adaptive matched filter(AMF)test and the RAO test,underwater monopulse AMF-DP-TBD algorithm and RAO-DP-TBD algorithm which incorporate persymmetry and symmetric spectrum,denoted as PSAMF-DP-TBD and PS-RAO-DP-TBD,are proposed and compared with the AMF-DP-TBD algorithm and RAO-DP-TBD algorithm based on persymmetry array,denoted as P-AMF-DP-TBD and P-RAO-DP-TBD.The simulation results show that the four methods can work normally with sufficient secondary data and slightly insufficient secondary data,but when the secondary data is severely insufficient,the P-AMF-DP-TBD and P-RAO-DP-TBD algorithms has failed while the PSAMF-DP-TBD and PS-RAO-DP-TBD algorithms still have good detection and tracking capabilities.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

基于状态向量增强ByteTrack的新生羔羊活动量自动计算方法

为评价母羊繁殖性能并及时发现分娩栏中的弱活力羔羊,该研究提出一种基于状态向量增强ByteTrack的新生羔羊活动量自动计算方法。针对传统ByteTrack算法在跟踪目标被遮挡时易发生身份切换的问题,引入置信度信息增强的状态向量,提高跟踪算法区分遮挡与被遮挡羔羊的能力。针对跟踪目标丢失导致轨迹预测不准确的问题,构建目标丢失期间的虚拟轨迹并重更新轨迹状态向量,以纠正轨迹误差。在获取各羔羊活动轨迹后,计算各羔羊帧间移动距离统计羔羊活动量。在江苏海门山羊研发中心采集的新生羔羊活动视频数据集上,测试状态向量增强的ByteTrack多目标跟踪算法性能。测试结果表明,研究提出的多目标跟踪方法在高阶跟踪精度、多目标跟踪精度、多目标跟踪准确度、IDF1得分上分别达到80.8%、86.1%、84.5%和92.2%,相较于现有算法的最高精度,分别提高2.7、0.2、2.3和3.9个百分点。该研究所提方法能够实现同窝多只新生羔羊的稳定跟踪,为新生羔羊活动量的自动计算、母羊繁殖性能的自动评估提供技术支撑。

基于改进YOLOv7-ByteTrack的干制哈密大枣缺陷检测与计数系统

针对目前无法同时对随机多列排布干制哈密大枣进行快速缺陷检测和统计计数问题,该研究设计了一款干制哈密大枣在线检测与计数系统。以干制哈密大枣为研究对象,利用工业相机拍摄传送带上随机排列的多类别缺陷干制哈密大枣视频为数据源,采用改进的YOLOv7模型进行干制哈密大枣多类别缺陷检测并将检测结果作为后续多目标跟踪算法的输入;考虑到传送带上干制哈密大枣的外观相似性高以及排列密集等特点,该研究结合ByteTrack多目标跟踪算法的思想,设计了一种多类别干制哈密大枣的画线计数方法,实现了随机排布多类别干制哈密大枣的缺陷检测、准确定位及计数。试验结果表明:1)改进的YOLOv7模型浮点计算量为64.6 G,在干制哈密大枣目标检测数据的测试集上的平均检测精度、召回率、F_(1)平衡分数分别达到了98.03%、93.43%和95.00%,相比YOLOv7模型分别提高了4.40、6.88和7.00个百分点,浮点计算量下降了38.6%;2)基于改进YOLOv7为目标检测器开发的ByteTrack算法计数模型对干制哈密大枣计数的准确率为90.12%。该研究可为干制哈密大枣检测计数和分选分级提供技术支持。

Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway management.However,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall accuracy.Deep learning is considered to be an efficient method for object detection in vision-based systems.In this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation technique.The model consists of six steps.In the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the images.These pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in images.Each detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple vehicles.Based on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching technique.Further,we implemented a Kalman filter to track multiple vehicles.In the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking algorithm.The experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art systems.The proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

基于自编码器结构与改进Bytetrack的低光照行人检测及跟踪算法

针对夜间低光照场景下目标特征提取困难和跟踪不稳定的问题,提出了基于自编码器结构及改进Bytetrack的多目标行人检测及跟踪算法。在检测阶段,基于YOLOX(you only look once X)搭建多任务自编码变换模型框架,以一种自监督的方式考虑物理噪声模型和图像信号处理(image signal processing,ISP)的过程,通过对真实光照退化变换过程进行编码与解码学习内在视觉结构,并基于这种表示通过解码边界框坐标与类实现目标检测任务。为了抑制背景噪声的干扰,在目标解码器颈部网络引入自适应特征融合模块ASFF。跟踪阶段,基于Bytetrack算法进行改进,将基于Tranformer重识别网络提取到的外观嵌入信息与NSA卡尔曼滤波获得的运动信息通过自适应加权的方法完成数据关联,并通过Byte两次匹配的算法完成夜间行人的跟踪。在自建夜间行人检测数据集上测试检测模型的泛化能力,mAP@0.5达到了94.9%,结果表明本文的退化变换过程符合现实条件,具有良好的泛化能力。最后通过自建夜间行人跟踪数据集验证多目标跟踪性能,实验结果表明,本文提出的夜间低光照行人多目标跟踪算法MOTA(multiple object tracking accuracy)为89.55%,IDF1(identity F1 score)为88.34%,IDs(ID switches)为15。与基准方法Bytetrack相比,MOTA提高了10.72%,IDF1提高了6.19%,IDs减少了50%。结果表明,本文提出的基于自编码结构及改进Bytetrack的多目标跟踪算法可以有效解决在夜间低光照场景下行人跟踪困难的问题。

改进YOLOv7+Bytetrack的小目标检测与追踪

近年来,目标检测技术已经相当成熟,但小目标检测一直是目标检测领域的一大挑战。为了解决这一问题,设计一种名为MFF-YOLOv7的小目标检测算法,该算法旨在提高小目标检测的准确率。设计级联双向特征金字塔KBiFPN,以及联合提出的多级感受野特征聚合模块MFA,来聚合浅层特征并增强特征的信息表达能力。为了解决小目标漏检问题,设计了新的解耦头和新的注意力机制。新的解耦头对小目标的检测能力更强,新的注意力机制可以重点关注感兴趣的小目标区域。引入了一种新的损失函数ECIOU,旨在加快模型的收敛速度。为了验证模型的性能,分别在三个小目标数据集上进行了实验。实验结果表明,MFF-YOLOv7算法提高了检测精度。同时,使用多目标追踪Bytetrack算法在MOT17和VisDrone2019-MOT两个多目标追踪数据集上对新模型进行了验证,进一步证明了其有效性。此外,MFF-YOLOv7算法在动态视频追踪中表现出了良好的性能。

Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

Simultaneous Multi-vehicle Detection and Tracking Framework with Pavement Constraints Based on Machine Learning and Particle Filter Algorithm

Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.

基于PigsTrack跟踪器的群养生猪多目标跟踪

基于视频的生猪行为跟踪和识别对于实现精细化养殖具有重要价值。为了应对群养生猪多目标跟踪任务中由猪只外观相似、遮挡交互等因素带来的挑战,研究提出了基于PigsTrack跟踪器的群养生猪多目标跟踪方法。PigsTrack跟踪器利用高性能YOLOX网络降低目标误检与漏检率,采用Transformer模型获取具有良好区分特性的目标外观特征;基于OC-SORT(observation-centric sort)的思想,通过集成特征匹配、IoU匹配和遮挡恢复匹配策略实现群养生猪的准确跟踪。基于PBVD(pigs behaviours video dataset)数据集的试验结果表明,PigsTrack跟踪器的HOTA(higher order tracking accuracy),MOTA(multiple object tracking accuracy)和IDF1得分(identification F1 score)分别为85.66%、98.59%和99.57%,相较于现有算法的最高精度,分别提高了3.71、0.03和2.05个百分点,证明了PigsTrack跟踪器在解决外观相似和遮挡交互引起的跟踪过程中身份跳变问题方面的有效性。随后,利用Slowfast网络对PigsTrack跟踪器的跟踪结果进行了典型行为统计,结果显示PigsTrack在群养生猪个体行为统计方面更准确。此外,通过在ABVD(aggressive-behavior video)数据集上的试验,PigsTrack跟踪器的HOTA、MOTA和IDF1得分分别为69.14%、94.82%和90.11%,相对于现有算法的最高精度,提高了5.33、0.57和8.60个百分点,验证了PigsTrack跟踪器在群养生猪跟踪任务中的有效性。总而言之,PigsTrack跟踪器能够有效应对外观相似和遮挡交互等挑战,实现了准确的生猪多目标跟踪,并在行为统计方面展现出更高的准确性,为生猪养殖领域的研究和实际应用提供了有价值的指导。

Surrounding Objects Detection and Tracking for Autonomous Driving Using LiDAR and Radar Fusion

Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles,Lidars are accurate in determining objects’positions but significantly less accurate as Radars on measuring their velocities.However,Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution.In order to compensate for the low detection accuracy,incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR,in this paper,an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles.By employing the Unscented Kalman Filter,Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle.Finally,the real vehicle test under various driving environment scenarios is carried out.The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy.Compared with a single sensor,it has obvious advantages and can improve the intelligence level of autonomous cars.

A robust system for real-time pedestrian detection and tracking

A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.

A Novel Tracking-by-Detection Method with Local Binary Pattern and Kalman Filter

Tracking-Learning-Detection( TLD) is an adaptive tracking algorithm,which tracks by learning the appearance of the object as the video progresses and shows a good performance in long-term tracking task.But our experiments show that under some scenarios,such as non-uniform illumination changing,serious occlusion,or motion-blurred,it may fails to track the object. In this paper,to surmount some of these shortages,especially for the non-uniform illumination changing,and give full play to the performance of the tracking-learning-detection framework, we integrate the local binary pattern( LBP) with the cascade classifiers,and define a new classifier named ULBP( Uniform Local Binary Pattern) classifiers. When the object appearance has rich texture features,the ULBP classifier will work instead of the nearest neighbor classifier in TLD algorithm,and a recognition module is designed to choose the suitable classifier between the original nearest neighbor( NN) classifier and the ULBP classifier. To further decrease the computing load of the proposed tracking approach,Kalman filter is applied to predict the searching range of the tracking object.A comprehensive study has been conducted to confirm the effectiveness of the proposed algorithm (TLD _ULBP),and different multi-property datasets were used. The quantitative evaluations show a significant improvement over the original TLD,especially in various lighting case.

Road boundary estimation to improve vehicle detection and tracking in UAV video

Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.

Architectural Building Detection and Tracking in Video Sequences Taken by Unmanned Aircraft System （UAS）

An Unmanned Aircraft System （UAS） is an aircraft or ground station that can be either remote controlled manually or is capable of flying autonomously under the guidance of pre-programmed Global Positioning System （GPS） waypoint flight plans or more complex onboard intelligent systems. The UAS aircrafts have recently found extensive applications in military reconnaissance and surveillance, homeland security, precision agriculture, fire monitoring and analysis, and other different kinds of aids needed in disasters. Through surveillance videos captured by a UAS digital imaging payload over the interest areas, the corresponding UAS missions can be conducted. In this paper, the authors present an effective method to detect and extract architectural buildings under rural environment from UAS video sequences. The SIFT points are chosen as image features. The planar homography is adopted as the motion model between different image frames. The proposed algorithm is tested on real UAS video data.

Hierarchical clustering based on single-pass for breaking topic detection and tracking

Single-pass is commonly used in topic detection and tracking( TDT) due to its simplicity,high efficiency and low cost. When dealing with large-scale data,time cost will increase sharply and clustering performance will be affected greatly. Aiming at this problem,hierarchical clustering algorithm based on single-pass is proposed,which is inspired by hierarchical and concurrent ideas to divide clustering process into three stages. News reports are classified into different categories firstly.Then there are twice single-pass clustering processes in the same category,and one agglomerative clustering among different categories. In addition,for semantic similarity in news reports,topic model is improved based on named entities. Experimental results show that the proposed method can effectively accelerate the process as well as improve the performance.

Object Detection and Tracking Method of AUV Based on Acoustic Vision

This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow （underwater acoustic images） is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.

面向多类别舰船多目标跟踪的改进CSTrack算法

针对海面舰船多目标跟踪过程中图像背景复杂、目标尺度差异大等难点,提出了一种改进CSTrack的舰船多目标跟踪算法。首先,针对CSTrack算法使用暴力解耦分解颈部特征造成目标特征损失的问题,提出了一种结合Res2net模块的改进互相关解耦网络RES_CCN,使网络解耦后获得更加细粒度的特征。其次,为提升对多类别舰船的跟踪性能,采用检测头网络解耦设计分别预测目标类别、置信度和位置。最后,采用MOT2016数据集进行消融实验,验证了所提模块的有效性,在新加坡海事数据集上进行测试,所提算法的多目标跟踪精度提升了8.4%,目标识别准确度提升了3.1%,优于ByteTrack等算法。本文所提算法具有跟踪精度高、误检率低等优点,适用于海面舰船多目标跟踪任务。