失物招领:失落的天使调查员

IF 0.3
Harsh Shrirame, Bhavesh Kewalramani, Daksh Kothari, Darshan Jawandhiya, Rina Damdoo
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引用次数: 0

摘要

印度每年都有大量儿童失踪。其中,由于警方面临文书工作繁重、技术匮乏等各种困难,大量案件从未侦破。因此,这项工作的主要目标之一是提供一个应用程序,可以帮助那些失踪的孩子被公众救出的人。这也将减少寻找失踪儿童所需的时间,使儿童尽快与亲人团聚。市民可以将受害儿童的照片和地标一起上传到我们的网络应用程序。如果数据库中存在失踪儿童的注册照片,这些照片将与之匹配。通过训练一个深度神经网络模型,利用市民上传的面部照片来定位走失的孩子。多任务CNN (MTCNN)是基于图像的应用程序中最有效的深度神经网络技术,用于面部识别。这些图像经过增强层得到不同方向、亮度和对比度的图像,这些图像被预先用于训练EfficientNetB0模型。然后用这个模型来识别照片中的人脸。将MTCNN模型与EfficientNetB0一起用于面部识别,并对其进行开发,产生了一个没有各种类型失真的深度学习模型。该模型的训练准确率为96.66%,测试准确率为76.81%,这意味着为失踪的孩子找到匹配的可能性约为77%。它是用25个Child类来评估的。每个Child类大约有15到20张图片。这些图像是在不同的背景和实时设置下拍摄的,因此即使图像中存在噪声,模型也可以工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lost + Found: The Lost Angel Investigator
Each year, a large number of youngsters are found missing in India. Among them, a large number of cases are never solved due to various difficulties faced by the police ranging from heavy paperwork to lacking technology. Therefore, one of this work’s key goals is to provide an application that may assist people whose children have been missing and rescued by the public. This will also reduce the time required to find the missing child to reunite the child with their loved ones as soon as possible. The pictures of child victims can be uploaded by the citizens along with landmarks, to our web app. The photographs will be matched to the missing child’s registered photographs if existing in the database. A deep neural network model is trained to locate the lost youngster using a facial picture uploaded by the citizens. Multi-Tasking CNN (MTCNN), the most efficient DNN technique for image-based apps, is used for facial Identification. The images were passed through an augmentation layer to get images of different orientations, brightness, and contrast, which were used ahead to train the EfficientNetB0 model. This model is then used to recognize faces in photographs. Using the MTCNN model for facial recognition with EfficientNetB0 and developing it yields a deep learning model that is free from all types of distortion. The model’s training accuracy is 96.66 percent and its testing accuracy is 76.81 percent, implying that there is approximately 77 percent possibility of finding a match for the missing kid. It was evaluated using 25 Child classes. Each Child class has around 15 to 20 images. These images are taken with different backgrounds and real-time settings so that model will work even when noise is present in the image.
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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