Object Detection using OpenCV and Deep Learning

Object Detection using SSD (Single Shot Detector) and MobileNets are efficient because this technique detects objects quickly with less resourses without sacrificing performance. In this every class of item for which the classification algorithm has been trained generates a bounding box and an annotation describing that class of object. This provides the foundation for creating several types of analytical features such as the volume of traffic in a certain area over time or the entire population in an area is real-time detection and categorization of objects from video data.

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Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Electronics ◽

10.3390/electronics10161932 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1932

Author(s):

Malik Haris ◽

Adam Glowacz

Keyword(s):

Image Processing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Large Scale ◽

Single Shot ◽

Automated Driving ◽

Convolutional Network ◽

Image Processing Algorithms ◽

Processing Algorithms

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

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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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Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

Remote Sensing ◽

10.3390/rs12010182 ◽

2020 ◽

Vol 12 (1) ◽

pp. 182 ◽

Cited By ~ 10

Author(s):

Lingxuan Meng ◽

Zhixing Peng ◽

Ji Zhou ◽

Jirong Zhang ◽

Zhenyu Lu ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Detection System ◽

Detection Model ◽

Aerial Vehicle ◽

Real Time Detection ◽

Pipeline Safety

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

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Real-Time and Deep Learning Based Vehicle Detection and Classification Using Pixel-Wise Code Exposure Measurements

Electronics ◽

10.3390/electronics9061014 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1014 ◽

Cited By ~ 3

Author(s):

Chiman Kwan ◽

David Gribben ◽

Bryan Chou ◽

Bence Budavari ◽

Jude Larkin ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Compressive Sensing ◽

Real Time ◽

Classification Performance ◽

Video Data ◽

Real Time System ◽

Processing Power ◽

Compressed Data ◽

Processing Device

One key advantage of compressive sensing is that only a small amount of the raw video data is transmitted or saved. This is extremely important in bandwidth constrained applications. Moreover, in some scenarios, the local processing device may not have enough processing power to handle object detection and classification and hence the heavy duty processing tasks need to be done at a remote location. Conventional compressive sensing schemes require the compressed data to be reconstructed first before any subsequent processing can begin. This is not only time consuming but also may lose important information in the process. In this paper, we present a real-time framework for processing compressive measurements directly without any image reconstruction. A special type of compressive measurement known as pixel-wise coded exposure (PCE) is adopted in our framework. PCE condenses multiple frames into a single frame. Individual pixels can also have different exposure times to allow high dynamic ranges. A deep learning tool known as You Only Look Once (YOLO) has been used in our real-time system for object detection and classification. Extensive experiments showed that the proposed real-time framework is feasible and can achieve decent detection and classification performance.

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An Approach on Image Processing of Deep Learning Based on Improved SSD

Symmetry ◽

10.3390/sym13030495 ◽

2021 ◽

Vol 13 (3) ◽

pp. 495

Author(s):

Liang Jin ◽

Guodong Liu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Remote Sensing Image ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Image Detection ◽

Ship Detection ◽

Real Time Detection

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

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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.

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Real-Time Deep Learning-Based Object Detection Framework

2020 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci47803.2020.9308493 ◽

2020 ◽

Author(s):

William Tarimo ◽

Moustafa M.Sabra ◽

Shonan Hendre

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time

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Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽

10.3390/s21124045 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4045

Author(s):

Alessandro Sassu ◽

Jose Francisco Saenz-Cogollo ◽

Maurizio Agelli

Keyword(s):

Deep Learning ◽

Real Time ◽

Video Data ◽

Video Analytics ◽

Web Based ◽

Real Time Analysis ◽

Open Source Framework ◽

Cluster Configuration ◽

Time Requirements ◽

High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

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