Intelligent Parking Management System Based on Image Processing

This paper aims to present an intelligent system for parking space detection based on image processing technique. The proposed system captures and processes the rounded image drawn at parking lot and produces the information of the empty car parking spaces. In this work, a camera is used as a sensor to take photos to show the occupancy of car parks. The reason why a camera is used is because with an image it can detect the presence of many cars at once. Also, the camera can be easily moved to detect different car parking lots. By having this image, the particular car parks vacant can be known and then the processed information was used to guide a driver to an available car park rather than wasting time to find one. The proposed system has been developed in both software and hardware platform. An automatic parking system is used to make the whole process of parking cars more efficient and less complex for both drivers and administrators.

Digital Signal Processing Based Real Time Vehicular Detection System

Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

This study develops a novel cervical precancerous detection system by using texture analysis of field emission scanning electron microscopy(FE-SEM)images.The processing scheme adopted in the proposed system focused on two steps.The first step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator.A problem arises from the question of how to extract features which characterize cervical precancerous cells.For the first step,a preprocessing technique called intensity transformation and morphological operation(ITMO)algorithm used to enhance the quality of images was proposed.The algo-rithm consisted of contrast stretching and morphological opening operations.The second step was to characterize the cervical cells to three classes,namely normal,low grade intra-epithelial squamous lesion(LSIL),and high grade intra-epithelial squamous lesion(HSIL).To differen-tiate between normal and precancerous cells of the cervical cell FE-SEM images,human papillomavirus(HPV)contained in the surface of cells were used as indicators.In this paper,we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture.Gray level co-occurrences matrix(GLCM)technique was used to extract the texture features.To confirm the system's perfor-mance,the system was tested using 150 cervical cell FE-SEM images.The results showed that the accuracy,sensitivity and specificity of the proposed system are 95.7%,95.7%and 95.8%,respectively.

Real-Time Lane Detection for Driver Assistance System

Traffic problem is more serious, as the number of vehicles is growing. Most of the road accidents were caused by carelessness of drivers. To reduce the number of traffic accidents and improve the safety and efficiency of traffic for many years around the world and company studies have been conducted on intelligent transport systems (ITS). Intelligent vehicle, (IV) the system is part of a system which is designed to assist drivers in the perception of any dangerous situations before, to avoid accidents after sensing and understanding the environment around itself. In this paper, it proposes architecture for driver assistance system based on image processing technology. To predict possible Lane departure, camera is mounted on the windshield of the car to determine the layout of roads and determines the position of the vehicle on line Lane. The resulting sequence of images is analyzed and processed by the proposed system, which automatically detects the Lane lines. The results showed of the proposed system to work well in a variety of settings, In addition computer response system is inexpensive and almost real time.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Advancing Deepfake Detection Using Xception Architecture:A Robust Approach for Safeguarding against Fabricated News on Social Media

Deepfake has emerged as an obstinate challenge in a world dominated by light.Here,the authors introduce a new deepfake detection method based on Xception architecture.The model is tested exhaustively with millions of frames and diverse video clips;accuracy levels as high as 99.65%are reported.These are the main reasons for such high efficacy:superior feature extraction capabilities and stable training mechanisms,such as early stopping,characterizing the Xception model.The methodology applied is also more advanced when it comes to data preprocessing steps,making use of state-of-the-art techniques applied to ensure constant performance.With an ever-rising threat from fake media,this piece of research puts great emphasis on stringent memory testing to keep at bay the spread of manipulated content.It also justifies better explanation methods to justify the reasoning done by the model for those decisions that build more trust and reliability.The ensemble models being more accurate have been studied and examined for establishing a possibility of combining various detection frameworks that could together produce superior results.Further,the study underlines the need for real-time detection tools that can be effective on different social media sites and digital environments.Ethics,protecting privacy,and public awareness in the fight against the proliferation of deepfakes are important considerations.By significantly contributing to the advancements made in the technology that has actually advanced detection,it strengthens the safety and integrity of the cyber world with a robust defense against ever-evolving deepfake threats in technology.Overall,the findings generally go a long way to prove themselves as the crucial step forward to ensuring information authenticity and the trustworthiness of society in this digital world.

Fast matching pursuit for traffic images using differential evolution

To obtain the sparse decomposition and flexible representation of traffic images,this paper proposes a fast matching pursuit for traffic images using differential evolution. According to the structural features of traffic images,the introduced algorithm selects the image atoms in a fast and flexible way from an over-complete image dictionary to adaptively match the local structures of traffic images and therefore to implement the sparse decomposition. As compared with the traditional method and a genetic algorithm of matching pursuit by using extensive experiments,the differential evolution achieves much higher quality of traffic images with much less computational time,which indicates the effectiveness of the proposed algorithm.

Research and Practice of Traffic Lights and Traffic Signs Recognition System Based on Multicore of FPGA

This thesis will present the research and practice of traffic lights and traffic signs recognition system based on multicore of FPGA. This system consists of four parts as following: the collection of dynamic images, the preprocessing of gray value, the detection of the edges and the patterning and the judgment of the pattern matching. The multiple cores system is consist of three cores. Each core parallels processes the incoming images from camera collection in terms of different colors and graphic elements. The image data read in from the camera works as the sharing data of the three cores.

基于复合图双卷积神经网络的路面裂缝识别方法

为了建立一种基于深度学习卷积神经网络的路面检测模型,提高特殊路面裂缝,如白裂缝、浅裂缝、潮湿裂缝、修补开裂等的识别准确率,在单卷积神经网络结构(单网络)上,提出了基于复合图双卷积神经网络的路面裂缝识别方法。首先,该方法在输入灰度图基础上,考虑裂缝病害图像特征,增加对应二值图组成复合图通道;其次,在单网络结构基础上增加一个针对特殊裂缝识别的单网络,非特殊裂缝网络训练使用全部数据,特殊裂缝网络训练使用特殊裂缝数据,两个网络参数分别独立更新,从而形成复合图双网络结构;然后两个网络分别对同一测试数据进行判定,得出各自的概率矩阵,最后再根据概率单侧抑制的原理将两个单网络输出结果进行叠加,得出最终识别结果。组织了70万张检测车采集图片对复合图双网络方法进行训练和测试。结果表明,复合图双网络识别重叠度、精确度、召回率显著优于灰度图单网络,证明了提出的两处优化,即将单通道灰度图改造为双通道复合输入图和增加一个特殊裂缝识别网络,提升了非特殊裂缝与特殊裂缝区域识别能力。此外,复合图双网络的重叠度、精确度、召回率指标比其他深度学习路面裂缝识别算法方法高。

基于机器视觉技术的鸡蛋新鲜度智能分级系统

为了对鸡蛋的新鲜度进行智能、便捷、无损、客观的检测,设计了基于机器视觉技术的鸡蛋新鲜度自动分级系统.系统利用图像处理算法智能读取电子秤显示的鸡蛋重量、测算鸡蛋的外部物理特征(纵径、横径、体积);由重量和体积计算密度,根据密度变化与储存时间之间的关系进行鸡蛋新鲜度的智能评价和分级.将87枚同一时间产出的鸡蛋样本作为模型集用于实验建模,40枚存放时间不同的鸡蛋样本作为测试集用于测试系统的可靠性.测试结果显示系统对测试集鸡蛋新鲜度智能分级的平均正确率为90.3%,可对禽蛋各项参数的智能检测提供技术支持.

基于改进YOLOv8n的茶叶嫩稍检测方法

针对名优茶智能采摘中茶叶嫩梢识别精度不足的问题,该研究对YOLOv8n模型进行优化。首先,在主干网络中引入动态蛇形卷积(dynamic snake convolution,DSConv),增强模型对茶叶嫩梢形状信息的捕捉能力;其次,将颈部的路径聚合网络(path aggregation network,PANet)替换为加权双向特征金字塔网络(bi-directional feature pyramid network,BiFPN),强化模型的特征融合效能;最后,在颈部网络的每个C2F模块后增设了无参注意力模块(simple attention module,SimAM),提升模型对茶叶嫩梢的识别关注度。试验结果表明,改进后的模型比原始模型的精确率(precision,P)、召回率(recall,R)、平均精确率均值(mean average precision,m AP)、F1得分(F1 score,F1)分别提升了4.2、2.9、3.7和3.3个百分点,推理速度为42帧/s,模型大小为6.7 MB,满足低算力移动设备的部署条件。与Faster-RCNN、YOLOv5n、YOLOv7n和YOLOv8n目标检测算法相比,该研究提出的改进模型精确率分别高出57.4、4.4、4.7和4.2个百分点,召回率分别高出53.0、3.6、2.8和2.9个百分点,平均精确率均值分别高出58.9、5.0、4.6和3.7个百分点,F1得分分别高出了56.8、3.9、3.7和3.3个百分点,在茶叶嫩梢检测任务中展现出了更高的精确度和更低的漏检率,能够为名优茶的智能采摘提供算法参考。

视频侦查技术关键及其发展展望

视频侦查技术的核心是通过视频图像的提取、查看、分析和研判来获取侦查线索、固定涉案证据。多年来经过在实战应用中不断地发展创新,视频侦查技术形成了自己独特的关键技术体系:视频信息分析解读技术是侦查应用和证据固定的基础和核心;视频证据固定保全技术的规范是审判中心主义的客观要求,可以获取视频侦查记录报告、视频检验鉴定报告或视频数据关联报告;低质量视频图像的增强恢复技术专业性强,应用范围窄,技术成熟度高,然而它不断面临新的挑战。目前,视频数据的智能应用在大数据背景下变得越来越重要,仍需进一步突破视频自动识别技术的应用范畴,建立完善多层次的视频数据综合应用体系,打造适应不同业务需要的视频数据实战应用模型。

无人机视角下交通小目标图像检测算法优化研究

针对无人机图像的小目标检测算法精度低、容易出现漏检和误检的情况,提出一种基于YOLOv7的改进目标检测算法。利用无人机航拍视频自建行人和车辆数据集。将颈部和检测头中的卷积模块替换为CoordConv模块,提高算法感知特征图的空间信息能力;添加小目标检测层,适应不同尺度下的物体目标,降低小目标的漏检率;在主干网络和颈部增加GE注意力机制,加强上下文信息的利用。将Wise-IoU作为边界框损失函数,引入一种动态非单调聚焦机制,提高模型的泛化能力。实验结果表明,改进后的算法精度高于实验中其他算法,精度达到91%,比YOLOv7算法提升了2.1个百分点。在VisDrone2019数据集上进行对比实验,精度比YOLOv7算法提升了2.5个百分点;综合性能优于实验中其他小目标检测算法,验证了改进后算法的泛化能力与有效性。

超厚纸张表面缺陷智能化图像处理与识别系统设计研究

设计一套智能化图像处理与识别系统,用于超厚纸张表面缺陷的检测,以解决传统超厚纸检测成本高、效率低、错误率高、精度低等问题。通过综合分析图像处理技术在超厚纸张表面缺陷检测中的可行性,从图像处理技术流程、应用可行性和系统设计等角度展开讨论,提出基于机器视觉的超厚纸张表面缺陷检测系统设计方案,包括整体设计、硬件环境和软件平台的选择与搭建,期望该系统能够高效、准确地检测超厚纸张表面的各类缺陷,提高超厚纸张生产质量和效率。

基于3D相机的轨道扣件部件丢失与松动智能检测

轨道扣件在运营过程中会出现松动甚至掉落、断裂等异常情况,不利于列车行驶稳定和安全,需要进行定期、及时的检查与维修。传统的人工巡检效率低,难以匹配我国轨道交通的快速发展,且对于部件松动等不易察觉的问题检测效果差。利用计算机视觉形成自动化的检测设备逐渐成为发展趋势,其中基于三角测量原理的线结构光技术因其成本低、精度高、速度快等优点得到广泛应用,且适合轨道检测场景。该技术核心设备为可以采集并分析线结构光进行三维重建的3D相机,基于成像原理设计可搭载于轨道检测车的扣件检测系统并进行现场试验,经过数据分析和处理可以分别得到高质量的图像数据和三维模型。针对图像数据利用目标检测的方法,构建数据集,搭载YOLO(You Only Look Once)v5深度学习模型,实现挡肩及扣件部件的快速识别,进行部件丢失检测;针对三维模型利用轨道扣件相对位置固定的特点,根据阈值筛选扣件数据并进一步得到弹条及螺栓等部件的坐标信息,通过边缘提取、平面拟合等方法计算位移量,进行部件松动检测。研究结果表明,检测系统可以采集高质量的扣件数据,扣件部件识别平均精准度达到99.0%,速度满足现场实时检测的要求,同时对于弹条和螺栓的松动量检测精度分别达到了1 mm和0.1 mm。该方法具有实际工程价值,可以大幅提升轨道巡检效率,对于扣件部件丢失、松动等严重问题可以及时预警指导修复,保障轨道安全服役性能。

基于YOLOv3的输电工程智能检测与分析技术研究

随着能源供应需求的不断增长以及人工智能技术的持续改进,对输电工程质量智能检测技术的要求也越来越高。针对上述问题,文中开展了基于YOLOv3的输电工程质量智能检测与分析技术研究。通过对采集到的输电工程样本数据进行预处理,经多次迭代计算,删除其中的凸包拐点数据。再将实时提取的单帧图像输入目标识别模型中,统计检测到的目标数量。并对低质量图片进行非线性自适应增强处理,以选取高斯双边函数计算均值信息,进而利用卷积算法完成对输电工程验收图片的滤波处理。在对传统的Faster-RCNN算法加以改进后,将FP-FRCNN模型嵌入密集连接结构中,实现输电工程质量智能检测。算例分析结果表明,所提方法可进行输电工程质量智能检测与分析处理,且检测精度高达99.35%,处理时间则仅为3.21 s。

基于影像视锥的二维三维一体雷视融合车速测量

为克服激光、相机在雷视监控设备测速任务中的采集频率、分辨率、视角等差异,提出一种影像优先与二维三维视图一体的多目标连续追踪测速方法。首先,均匀选取场景几何特征同名点对,采用直接线性变换法计算外参参数,在线完成相机与激光雷达间标定;然后,提出视觉引导的视锥体空间下二维三维一体目标定位方法,以高分辨率地面点计算区域界限初步去除背景点,将视锥三维视图转换为二维俯视图自适应计算目标聚类参数,以二维最密体素指引恢复至三维选取起始种子点完成聚类定位以消除无关点,解决视角导致的不同分辨率点云目标检测混淆问题;最后,基于卡尔曼滤波,考虑车辆运动状态,根据离散同步帧点云定位结果将测速过程建模为相应观测方程,并以点云分辨率估计观测噪声协方差矩阵,实现对离散测速结果的连续最优估计,解决观测噪声以及两设备时间异步的误差影响。在常见交通场景的图像+点云数据下进行实验,结果表明,本文方法速度测量的平均绝对误差为0.2764 m/s,均方根误差为0.3251 m/s,最远探测距离达到103.211 m,具有较高的准确性和实用性。

基于改进YOLOv5车辆检测方法

针对现有目标检测在智能交通系统和自动驾驶等领域存在车辆目标检测精度低、鲁棒性较差等问题,提出一种基于YOLOv5的车辆目标检测算法。在YOLOv5s网络模型框架中,添加注意力机制增强特征,提取重要特征;添加小目标检测层提升对遮挡重叠弱小目标识别的准确率;引入金字塔池化(SPPFCSPC),提高网络空间特征提取能力;引入损失函数(SIoU_Loss)加快边界框回归速率,提高定位精度,消除重叠检测。基于自制车辆检测数据集进行实验,其结果表明,改进网络模型与原YOLOv5s网络模型相比,不同目标类的平均准确率均有明显提高,平均准确率均值提升3.25%,查准率提高4.14%,召回率提高3.05%,检测速度满足实时性要求。