Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image processing.Medical science combined with artificial intelligence is advanc...Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image processing.Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases.Key to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and treatment.This study makes a small contribution to endoscopic image classification.The proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception.Additionally,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature vectors.The fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)classifier.The Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were used.Performance assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private dataset.This approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.展开更多
远程塔台由于其低成本高时效远程实时控制技术正越来越受到民航业界的青睐,其中运动目标自动检测和显示是远程塔台的核心技术,作为增强现实技术更好地为管制员提供服务。在分析远程塔台机场场面背景复杂、场面目标多为远场景、小目标等...远程塔台由于其低成本高时效远程实时控制技术正越来越受到民航业界的青睐,其中运动目标自动检测和显示是远程塔台的核心技术,作为增强现实技术更好地为管制员提供服务。在分析远程塔台机场场面背景复杂、场面目标多为远场景、小目标等特点基础上,提出了一种改进的You Only Look Once(YOLO)算法来实现远程塔台运动目标的检测,算法核心思想以Darknet-53为基础网络,多尺度预测边界框,以运动目标图像坐标的偏移量作为边框长宽的线性变换来实现边框的回归,改善了传统YOLO算法损失函数不同大小的边框未做区分的问题,提高了检测准确性和速度。机场真实数据实验表明,该算法能快速、准确的检测出远程塔台的运动目标,并准确的回归运动目标边框及分类。展开更多
针对新冠肺炎防控期间肉眼识别判断行人是否佩戴口罩效率低且存在较大风险的问题,提出一种改进检测目标边框损失的自然场景下行人是否佩戴口罩的检测算法.该算法对YOLOv3损失函数进行改进,应用GIoU计算目标边界框损失,完成自然场景下行...针对新冠肺炎防控期间肉眼识别判断行人是否佩戴口罩效率低且存在较大风险的问题,提出一种改进检测目标边框损失的自然场景下行人是否佩戴口罩的检测算法.该算法对YOLOv3损失函数进行改进,应用GIoU计算目标边界框损失,完成自然场景下行人是否佩戴口罩的检测.算法在开源的WIDER FACE数据集和MAFA数据集上训练,采集自然场景图片进行测试,行人是否佩戴口罩的mAP(mean Average Precision)达到了88.4%,取得了较高的检测准确率,在自然场景视频检测中平均每秒传输帧数达到38.69,满足实时检测的要求.展开更多
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and Granted Financial Resources from the Ministry of Trade,Industry,and Energy,Korea(No.20204010600090).
文摘Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image processing.Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases.Key to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and treatment.This study makes a small contribution to endoscopic image classification.The proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception.Additionally,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature vectors.The fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)classifier.The Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were used.Performance assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private dataset.This approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.
文摘远程塔台由于其低成本高时效远程实时控制技术正越来越受到民航业界的青睐,其中运动目标自动检测和显示是远程塔台的核心技术,作为增强现实技术更好地为管制员提供服务。在分析远程塔台机场场面背景复杂、场面目标多为远场景、小目标等特点基础上,提出了一种改进的You Only Look Once(YOLO)算法来实现远程塔台运动目标的检测,算法核心思想以Darknet-53为基础网络,多尺度预测边界框,以运动目标图像坐标的偏移量作为边框长宽的线性变换来实现边框的回归,改善了传统YOLO算法损失函数不同大小的边框未做区分的问题,提高了检测准确性和速度。机场真实数据实验表明,该算法能快速、准确的检测出远程塔台的运动目标,并准确的回归运动目标边框及分类。
文摘针对新冠肺炎防控期间肉眼识别判断行人是否佩戴口罩效率低且存在较大风险的问题,提出一种改进检测目标边框损失的自然场景下行人是否佩戴口罩的检测算法.该算法对YOLOv3损失函数进行改进,应用GIoU计算目标边界框损失,完成自然场景下行人是否佩戴口罩的检测.算法在开源的WIDER FACE数据集和MAFA数据集上训练,采集自然场景图片进行测试,行人是否佩戴口罩的mAP(mean Average Precision)达到了88.4%,取得了较高的检测准确率,在自然场景视频检测中平均每秒传输帧数达到38.69,满足实时检测的要求.
