Review of Deep Learning Techniques for Object Detection and Classification

2018 ◽  
pp. 422-431 ◽  
Author(s):  
Mohd Ali Ansari ◽  
Dushyant Kumar Singh
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Techniques

scholarly journals Deep Learning for Generic Object Detection: A Survey

2019 ◽  
Vol 128 (2) ◽  
pp. 261-318 ◽  
Author(s):  
Li Liu ◽  
Wanli Ouyang ◽  
Xiaogang Wang ◽  
Paul Fieguth ◽  
Jie Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Rapid Evolution ◽  
Feature Representation ◽  
Future Research ◽  
Context Modeling ◽  
Feature Representations ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Object Feature

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

scholarly journals Identify Light-curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection

2021 ◽  
Vol 163 (1) ◽  
pp. 23
Author(s):  
Kaiming Cui ◽  
Junjie Liu ◽  
Fabo Feng ◽  
Jifeng Liu
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Human Visual Perception ◽  
Lower Confidence ◽  
Learning Techniques ◽  
Confidence Threshold ◽  
Bounding Boxes ◽  
Python Package

Abstract Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well-proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields about 90% precision and recall for identifying transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). Giving a slightly lower confidence threshold, recall can reach higher than 95%. We also transfer the trained model to the TESS data and obtain similar performance. The results of our algorithm match the intuition of the human visual perception and make it useful to find single-transiting candidates. Moreover, the parameters of the output bounding boxes can also help to find multiplanet systems. Our network and detection functions are implemented in the Deep-Transit toolkit, which is an open-source Python package hosted on Github and PyPI.

scholarly journals Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

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.

scholarly journals Digital Naturalist Using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 2061-2065
Author(s):  
Aparna .
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Object Detection ◽  
Wild Species ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Semantic Segmentation ◽  
Model Image ◽  
Image Sensing ◽  
Learning Techniques

A naturalist is someone who studies the patterns of nature identify different kingdom of flora and fauna in the nature. Being able to identify the flora and fauna around us often leads to an interest in protecting wild species, collecting and sharing information about the species we see on our travels is very useful for conserving groups like NCC. Deep-learning based techniques and methods are becoming popular in digital naturalist studies, as their performance is superior in image analysis fields, such as object detection, image classification, and semantic segmentation. Deep-learning techniques have achieved state of-the -art performance for automatic segmentation of digital naturalist through multi-model image sensing. Our task as naturalist has grown widely in the field of natural-historians. It has increased from identification to saviours as well. Not only identifying flora and fauna but also to know about their habits, habitats, living and grouping lead to fetching services for protection as well.

scholarly journals Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

2019 ◽  
Author(s):  
Ellen M. Ditria ◽  
Sebastian Lopez-Marcano ◽  
Michael K. Sievers ◽  
Eric L. Jinks ◽  
Christopher J. Brown ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Fish Assemblages ◽  
Aquatic Ecology ◽  
Video Data ◽  
Fish Abundance ◽  
Data Sets ◽  
Seagrass Meadows ◽  
Learning Techniques ◽  
Better Than

AbstractAquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomised 80:20 ratios of training:validation data-sets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%), and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer’s performance in determining MaxN was 7.1% better than human marine experts, and 13.4% better than citizen scientists in single image test data-sets, and 1.5% and 7.8% higher in video data-sets, respectively. We show that deep learning is a more accurate tool than humans at determining abundance, and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance. Deep learning techniques have much to offer the field of aquatic ecology.

A Review on Deep Learning Techniques Applied to Object Detection

2020 ◽  
Author(s):  
Ying-jie Liang ◽  
Xiao-peng Cui ◽  
Xing-hua Xu ◽  
Feng Jiang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Techniques

scholarly journals Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review

2020 ◽  
Vol 10 (9) ◽  
pp. 3280 ◽  
Author(s):  
Chinthakindi Balaram Murthy ◽  
Mohammad Farukh Hashmi ◽  
Neeraj Dhanraj Bokde ◽  
Zong Woo Geem
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Pedestrian Detection ◽  
Detection Methods ◽  
Art Object ◽  
Current State ◽  
Learning Techniques ◽  
Specific Object ◽  
Benchmark Datasets ◽  
Speed Up

In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions.

scholarly journals A Framework for Object Detection using Deep-Reinforcement Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1508-1514
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Object Detection ◽  
Intelligent Systems ◽  
Learning Algorithm ◽  
Efficient Tool ◽  
Narrow Scope ◽  
Deep Learning Algorithm ◽  
Learning Techniques

Machine learning area enable the utilization of Deep learning algorithm and neural networks (DNNs) with Reinforcement Learning. Reinforcement learning and DL both is region of AI, it’s an efficient tool towards structuring artificially intelligent systems and solving sequential deciding problems. Reinforcement learning (RL) deals with the history of moves; Reinforcement learning problems are often resolve by an agent often denoted as (A) it has privilege to make decisions during a situation to optimize a given problem by collective rewards. Ability to structure sizable amount of attributes make deep learning an efficient tool for unstructured data. Comparing multiple deep learning algorithms may be a major issue thanks to the character of the training process and therefore the narrow scope of datasets tested in algorithmic prisons. Our research proposed a framework which exposed that reinforcement learning techniques in combination with Deep learning techniques learn functional representations for sorting problems with high dimensional unprocessed data. The faster RCNN model typically founds objects in faster way saving resources like computation, processing, and storage. But still object detection technique typically require high computation power and large memory and processor building it hard to run on resource constrained devices (RCD) for detecting an object during real time without an efficient and high computing machine.

Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey

2018 ◽  
Vol 35 (1) ◽  
pp. 84-100 ◽  
Author(s):  
Junwei Han ◽  
Dingwen Zhang ◽  
Gong Cheng ◽  
Nian Liu ◽  
Dong Xu
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Techniques ◽  
Specific Object
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