Solar Event Detection Using Deep-Learning-Based Object Detection Methods

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Electronics ◽

10.3390/electronics10040517 ◽

2021 ◽

Vol 10 (4) ◽

pp. 517

Author(s):

Seong-heum Kim ◽

Youngbae Hwang

Keyword(s):

Deep Learning ◽

Object Detection ◽

Low Cost ◽

Detection Methods ◽

Future Research ◽

3D Object ◽

Practical Applications ◽

Depth Sensors ◽

Significant Research ◽

3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

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Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Journal of Imaging ◽

10.3390/jimaging7090176 ◽

2021 ◽

Vol 7 (9) ◽

pp. 176

Author(s):

Daniel Queirós da Silva ◽

Filipe Neves dos Santos ◽

Armando Jorge Sousa ◽

Vítor Filipe

Keyword(s):

Deep Learning ◽

Object Detection ◽

Forest Inventory ◽

Mobile Robotics ◽

Ground Level ◽

Forest Tree ◽

Thermal Image ◽

Detection Methods ◽

Site Management ◽

Forest Wildfires

Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

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Deep Learning Approaches for Object Detection

Artificial Intelligence Evolution ◽

10.37256/aie.122020564 ◽

2020 ◽

pp. 123-145

Author(s):

Sushma Jaiswal ◽

Tarun Jaiswal

Keyword(s):

Deep Learning ◽

Object Detection ◽

Autonomous Driving ◽

General Idea ◽

Detection Methods ◽

Learning Approaches ◽

Detection Techniques ◽

Second Stage ◽

Fast Pace ◽

Benchmark Datasets

In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.

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Video Object Detection through Traditional and Deep Learning Methods

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d6833.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1822-1826

Keyword(s):

Deep Learning ◽

Object Detection ◽

Sparse Matrix ◽

Image Understanding ◽

Low Rank ◽

Detection Methods ◽

Video Object ◽

Learning Methods ◽

Primary Focus ◽

High Level

Object detection in videos is gaining more attention recently as it is related to video analytics and facilitates image understanding and applicable to . The video object detection methods can be divided into traditional and deep learning based methods. Trajectory classification, low rank sparse matrix, background subtraction and object tracking are considered as traditional object detection methods as they primary focus is informative feature collection, region selection and classification. The deep learning methods are more popular now days as they facilitate high-level features and problem solving in object detection algorithms. We have discussed various object detection methods and challenges in this paper.

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Ship Detection Based on YOLOv2 for SAR Imagery

Remote Sensing ◽

10.3390/rs11070786 ◽

2019 ◽

Vol 11 (7) ◽

pp. 786 ◽

Cited By ~ 41

Author(s):

Yang-Lang Chang ◽

Amare Anagaw ◽

Lena Chang ◽

Yi Wang ◽

Chih-Yu Hsiao ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Experimental Results ◽

Detection Methods ◽

Computational Time ◽

Detection Accuracy ◽

Single Shot ◽

Ship Detection ◽

Sar Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.

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Geometry-Aware Cell Detection with Deep Learning

mSystems ◽

10.1128/msystems.00840-19 ◽

2020 ◽

Vol 5 (1) ◽

Cited By ~ 3

Author(s):

Hao Jiang ◽

Sen Li ◽

Weihuang Liu ◽

Hongjin Zheng ◽

Jinghao Liu ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Detection Methods ◽

Biological Research ◽

Geometric Feature ◽

Cell Detection ◽

Learning Method ◽

Key Points ◽

Cell Target ◽

Cell Shapes

ABSTRACT Analyzing cells and tissues under a microscope is a cornerstone of biological research and clinical practice. However, the challenge faced by conventional microscopy image analysis is the fact that cell recognition through a microscope is still time-consuming and lacks both accuracy and consistency. Despite enormous progress in computer-aided microscopy cell detection, especially with recent deep-learning-based techniques, it is still difficult to translate an established method directly to a new cell target without extensive modification. The morphology of a cell is complex and highly varied, but it has long been known that cells show a nonrandom geometrical order in which a distinct and defined shape can be formed in a given type of cell. Thus, we have proposed a geometry-aware deep-learning method, geometric-feature spectrum ExtremeNet (GFS-ExtremeNet), for cell detection. GFS-ExtremeNet is built on the framework of ExtremeNet with a collection of geometric features, resulting in the accurate detection of any given cell target. We obtained promising detection results with microscopic images of publicly available mammalian cell nuclei and newly collected protozoa, whose cell shapes and sizes varied. Even more striking, our method was able to detect unicellular parasites within red blood cells without misdiagnosis of each other. IMPORTANCE Automated diagnostic microscopy powered by deep learning is useful, particularly in rural areas. However, there is no general method for object detection of different cells. In this study, we developed GFS-ExtremeNet, a geometry-aware deep-learning method which is based on the detection of four extreme key points for each object (topmost, bottommost, rightmost, and leftmost) and its center point. A postprocessing step, namely, adjacency spectrum, was employed to measure whether the distances between the key points were below a certain threshold for a particular cell candidate. Our newly proposed geometry-aware deep-learning method outperformed other conventional object detection methods and could be applied to any type of cell with a certain geometrical order. Our GFS-ExtremeNet approach opens a new window for the development of an automated cell detection system.

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

Applied Sciences ◽

10.3390/app10093280 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3280 ◽

Cited By ~ 3

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.

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A brief review and challenges of object detection in optical remote sensing imagery

Multiagent and Grid Systems ◽

10.3233/mgs-200330 ◽

2020 ◽

Vol 16 (3) ◽

pp. 227-243

Author(s):

Shahid Karim ◽

Ye Zhang ◽

Shoulin Yin ◽

Irfana Bibi ◽

Ali Anwar Brohi

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Detection Methods ◽

Optical Remote Sensing ◽

Single Shot ◽

Processing Efficiency ◽

Regression Problem ◽

Detection Algorithms ◽

Sensing Applications

Traditional object detection algorithms and strategies are difficult to meet the requirements of data processing efficiency, performance, speed and intelligence in object detection. Through the study and imitation of the cognitive ability of the brain, deep learning can analyze and process the data features. It has a strong ability of visualization and becomes the mainstream algorithm of current object detection applications. Firstly, we have discussed the developments of traditional object detection methods. Secondly, the frameworks of object detection (e.g. Region-based CNN (R-CNN), Spatial Pyramid Pooling Network (SPP-NET), Fast-RCNN and Faster-RCNN) which combine region proposals and convolutional neural networks (CNNs) are briefly characterized for optical remote sensing applications. You only look once (YOLO) algorithm is the representative of the object detection frameworks (e.g. YOLO and Single Shot MultiBox Detector (SSD)) which transforms the object detection into a regression problem. The limitations of remote sensing images and object detectors have been highlighted and discussed. The feasibility and limitations of these approaches will lead researchers to prudently select appropriate image enhancements. Finally, the problems of object detection algorithms in deep learning are summarized and the future recommendations are also conferred.

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Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset

Lontar Komputer Jurnal Ilmiah Teknologi Informasi ◽

10.24843/lkjiti.2020.v11.i03.p03 ◽

2020 ◽

Vol 11 (3) ◽

pp. 144

Author(s):

Yonatan Adiwinata ◽

Akane Sasaoka ◽

I Putu Agung Bayupati ◽

Oka Sudana

Keyword(s):

Deep Learning ◽

Object Detection ◽

Fish Species ◽

Species Recognition ◽

Species Conservation ◽

Detection Methods ◽

Single Shot ◽

Natural Ecosystems ◽

Efficient Technology

Fish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep learning methods used were Convolutional Layer or Convolutional Neural Network (CNN). This research experimented with using object detection method based on deep learning like Faster R-CNN, which possible to recognize the species of fish inside of the image without more image preprocessing. This research aimed to know the performance of the Faster R-CNN method against other object detection methods like SSD in fish species detection. The fish dataset used in the research reference was QUT FISH Dataset. The accuracy of the Faster R-CNN reached 80.4%, far above the accuracy of the Single Shot Detector (SSD) Model with an accuracy of 49.2%.

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