Tasks of Object Detection using Deep Learning Architectures

Deep learning is a subset of the field of machine learning, which is a subfield of AI. The facts that differentiate deep learning networks in general from “canonical” feedforward multilayer networks are More neurons than previous networks, More complex ways of connecting layers, “Cambrian explosion” of computing power to train and Automatic feature extraction. Deep learning is defined as neural networks with a large number of parameters and layers in fundamental network architectures. Some of the network architectures are Convolutional Neural Networks, Recurrent Neural Networks Recursive Neural Networks, RCNN (Region Based CNN), Fast RCNN, Google Net, YOLO (You Only Look Once), Single Shot detectors, SegNet and GAN (Generative Adversarial Network). Different architectures work well with different types of Datasets. Object Detection is an important computer vision problem with a variety of applications. The tasks involved are classification, Object Localisation and instance segmentation. This paper will discuss how the different architectures are useful to detect the object.

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

RSC Advances ◽

10.1039/d1ra06905e ◽

2022 ◽

Vol 12 (3) ◽

pp. 1769-1776

Author(s):

Ruizhao Yang ◽

Yun Li ◽

Binyi Qin ◽

Di Zhao ◽

Yongjin Gan ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Terahertz Spectroscopy ◽

Learning Networks ◽

Pesticide Detection ◽

Generative Adversarial Network ◽

Adversarial Network

We proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy.

Learning Cartographic Building Generalization with Deep Convolutional Neural Networks

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060258 ◽

2019 ◽

Vol 8 (6) ◽

pp. 258 ◽

Cited By ~ 13

Author(s):

Yu Feng ◽

Frank Thiemann ◽

Monika Sester

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Physical Reality ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Cartographic Generalization

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance.

Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4225.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1498-1508

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Object Detection ◽

Learning Algorithm ◽

Pedestrian Detection ◽

Single Shot ◽

Deep Learning Algorithm ◽

Learning Techniques ◽

Rgb Images ◽

Deep Convolution Network

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.

Hardware Trojan Detection Using Deep Learning-Generative Adversarial Network and Stacked Auto Encoder Neural Networks

ICT Analysis and Applications - Lecture Notes in Networks and Systems ◽

10.1007/978-981-16-5655-2_19 ◽

2022 ◽

pp. 203-210

Author(s):

Fredin Jose ◽

M. Priyatharishini ◽

M. Nirmala Devi

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Hardware Trojan ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Hardware Trojan Detection ◽

Trojan Detection

SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing

Sensors ◽

10.3390/s20143918 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3918

Author(s):

Noi Quang Truong ◽

Young Won Lee ◽

Muhammad Owais ◽

Dat Tien Nguyen ◽

Ganbayar Batchuluun ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Processing Time ◽

Detection Method ◽

Uniform Motion ◽

Two Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Motion Deblurring ◽

Marker Detection

Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.

Seismic image processing through the generative adversarial network

Interpretation ◽

10.1190/int-2018-0232.1 ◽

2019 ◽

Vol 7 (3) ◽

pp. SF15-SF26

Author(s):

Francesco Picetti ◽

Vincenzo Lipari ◽

Paolo Bestagini ◽

Stefano Tubaro

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Proof Of Concept ◽

High Quality ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Seismic Image ◽

Geophysical Imaging

The advent of new deep-learning and machine-learning paradigms enables the development of new solutions to tackle the challenges posed by new geophysical imaging applications. For this reason, convolutional neural networks (CNNs) have been deeply investigated as novel tools for seismic image processing. In particular, we have studied a specific CNN architecture, the generative adversarial network (GAN), through which we process seismic migrated images to obtain different kinds of output depending on the application target defined during training. We have developed two proof-of-concept applications. In the first application, a GAN is trained to turn a low-quality migrated image into a high-quality one, as if the acquisition geometry was much more dense than in the input. In the second example, the GAN is trained to turn a migrated image into the respective deconvolved reflectivity image. The effectiveness of the investigated approach is validated by means of tests performed on synthetic examples.

Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis

Complexity ◽

10.1155/2021/5541134 ◽

2021 ◽

Vol 2021 ◽

pp. 1-30

Author(s):

R. Nandhini Abirami ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Usman Tariq ◽

Chuan-Yu Chang

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Visual Perception ◽

Generative Adversarial Networks ◽

Deep Convolutional Neural Networks ◽

Generative Adversarial Network ◽

Biological Vision ◽

Adversarial Network ◽

Adversarial Networks

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides.

Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

Symmetry ◽

10.3390/sym12101718 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1718

Author(s):

Chien-Hsing Chou ◽

Yu-Sheng Su ◽

Che-Ju Hsu ◽

Kong-Chang Lee ◽

Ping-Hsuan Han

Keyword(s):

Deep Learning ◽

Object Detection ◽

User Experience ◽

Recognition System ◽

Scene Recognition ◽

Single Shot ◽

Auditory Signals ◽

Hot Weather ◽

Viewing Experience ◽

At Home

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽

10.3390/s20061579 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1579

Author(s):

Dongqi Wang ◽

Qinghua Meng ◽

Dongming Chen ◽

Hupo Zhang ◽

Lisheng Xu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Channel Model ◽

Expert Knowledge ◽

Automatic Detection ◽

Data Representation ◽

Learning Technology ◽

Arrhythmia Detection ◽

Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽

10.3390/info12060249 ◽

2021 ◽

Vol 12 (6) ◽

pp. 249

Author(s):

Xin Jin ◽

Yuanwen Zou ◽

Zhongbing Huang

Keyword(s):

Cell Cycle ◽

Deep Learning ◽

Image Classification ◽

Classification Accuracy ◽

Data Augmentation ◽

Cycle Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.