scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals Brand Detection System using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 497-500
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Visual Representation ◽  
Cosmetic Products ◽  
Medical Object

In this paper a method of recognizing logos of the brand of cosmetic products using deep learning. There are several of hoax product which easily copies the famous brand’s logo and deteriorates the company’s image. The machine learning has proved to be useful in various of the fields like medical, object detection, vehicle logo recognitions. But till now very few of the works have been performed in cosmetic field. This field is covered using the model sequential convolutional neural network using Tensorflow and Keras. For the visual representation of the result Tensorboard is used. Work have been started with two of the brands-Lakme and L’Oreal. Depending upon the success of this technique, further brands for logo may be added for recognition. The accuracy of approximately 80% was obtained using this technique.

scholarly journals 3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171461-171470
Author(s):  
Dianwei Wang ◽  
Yanhui He ◽  
Ying Liu ◽  
Daxiang Li ◽  
Shiqian Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
3D Object ◽  
Multi Scale ◽  
Panoramic Images ◽  
3D Object Detection

scholarly journals MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network

2020 ◽  
Vol 12 (22) ◽  
pp. 9785
Author(s):  
Kisu Lee ◽  
Goopyo Hong ◽  
Lee Sael ◽  
Sanghyo Lee ◽  
Ha Young Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Structural Integrity ◽  
Residential Building ◽  
Training Environment ◽  
Building Facade ◽  
Building Facades

Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.

Sign Board Recognition Based on Convolutional Neural Network Using Yolo-3

2020 ◽  
Vol 17 (8) ◽  
pp. 3478-3483
Author(s):  
V. Sravan Chowdary ◽  
G. Penchala Sai Teja ◽  
D. Mounesh ◽  
G. Manideep ◽  
C. T. Manimegalai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Road Accidents ◽  
Learning Models ◽  
The Neural Network

Road injuries are a big drawback in society for a few time currently. Ignoring sign boards while moving on roads has significantly become a major cause for road accidents. Thus we came up with an approach to face this issue by detecting the sign board and recognition of sign board. At this moment there are several deep learning models for object detection using totally different algorithms like RCNN, faster RCNN, SPP-net, etc. We prefer to use Yolo-3, which improves the speed and precision of object detection. This algorithm will increase the accuracy by utilizing residual units, skip connections and up-sampling. This algorithm uses a framework named Dark-net. This framework is intended specifically to create the neural network for training the Yolo algorithm. To thoroughly detect the sign board, we used this algorithm.

scholarly journals Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss

2019 ◽  
Vol 11 (2) ◽  
pp. 43
Author(s):  
Samuel Aji Sena ◽  
Panca Mudjirahardjo ◽  
Sholeh Hadi Pramono
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Ductal Carcinoma ◽  
Detection System ◽  
Breast Cancer Detection ◽  
University Of Pennsylvania ◽  
Training Approach

This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach.

scholarly journals Image target detection algorithm compression and pruning based on neural network

2021 ◽  
Vol 18 (2) ◽  
pp. 499-516
Author(s):  
Yan Sun ◽  
Zheping Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Network Model ◽  
Detection Algorithm ◽  
Image Feature ◽  
Propagation Mode ◽  
Learning Technology ◽  
Image Detection ◽  
Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

scholarly journals SBC Based Object and Text Recognition Wearable System u sing Convolutional Neural Network with Deep Learning Algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 198-205
Author(s):  
Melchiezhedhieck J. Bongao ◽  
◽  
Arvin F. Almadin ◽  
Christian L. Falla ◽  
Juan Carlo F. Greganda ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Reading Ability ◽  
Visually Impaired ◽  
City Government ◽  
Text Recognition ◽  
Status Report ◽  
Visually Impaired Persons

This Raspberry Single-Board Computer-Based Object and Text Real-time Recognition Wearable Device using Convolutional Neural Network through TensorFlow Deep Learning, Python and C++ programming languages, and SQLite database application, which detect stationary objects, road signs and Philippine (PHP) money bills, and recognized texts through camera and translate it to audible outputs such as English and Filipino languages. Moreover, the system has a battery notification status using an Arduino microcontroller unit. It also has a switch for object detection mode, text recognition mode, and battery status report mode. This could fulfill the incapability of visually impaired in identifying of objects and the lack of reading ability as well as reducing the assistance that visually impaired needs. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models were used as the methodologies of this study. Visually impaired persons and the Persons with Disability Affairs Office of the City Government of Biñan, Laguna, Philippines served as the main respondents of the survey conducted. Obtained results stipulated that the object detection, text recognition, and its attributes were accurate and reliable, which gives a significant distinction from the current system to detect objects and recognize printed texts for the visually impaired people.

Weapon and Object Detection Using Mobile-Net SSD Model in Deep Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 1573-1582
Author(s):  
S Gopi Naik
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Optimization Algorithms ◽  
Object Identification ◽  
Integrated System ◽  
Raspberry Pi ◽  
High Level

Abstract: The plan is to establish an integrated system that can manage high-quality visual information and also detect weapons quickly and efficiently. It is obtained by integrating ARM-based computer vision and optimization algorithms with deep neural networks able to detect the presence of a threat. The whole system is connected to a Raspberry Pi module, which will capture live broadcasting and evaluate it using a deep convolutional neural network. Due to the intimate interaction between object identification and video and image analysis in real-time objects, By generating sophisticated ensembles that incorporate various low-level picture features with high-level information from object detection and scenario classifiers, their performance can quickly plateau. Deep learning models, which can learn semantic, high-level, deeper features, have been developed to overcome the issues that are present in optimization algorithms. It presents a review of deep learning based object detection frameworks that use Convolutional Neural Network layers for better understanding of object detection. The Mobile-Net SSD model behaves differently in network design, training methods, and optimization functions, among other things. The crime rate in suspicious areas has been reduced as a consequence of weapon detection. However, security is always a major concern in human life. The Raspberry Pi module, or computer vision, has been extensively used in the detection and monitoring of weapons. Due to the growing rate of human safety protection, privacy and the integration of live broadcasting systems which can detect and analyse images, suspicious areas are becoming indispensable in intelligence. This process uses a Mobile-Net SSD algorithm to achieve automatic weapons and object detection. Keywords: Computer Vision, Weapon and Object Detection, Raspberry Pi Camera, RTSP, SMTP, Mobile-Net SSD, CNN, Artificial Intelligence.

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