Motion object detection method based on real-time background update under complex environment

To tackle the water surface pollution problem, a vision-based water surface garbage capture robot has been developed in our lab. In this article, we present a modified you only look once v3-based garbage detection method, allowing real-time and high-precision object detection in dynamic aquatic environments. More specifically, to improve the real-time detection performance, the detection scales of you only look once v3 are simplified from 3 to 2. Besides, to guarantee the accuracy of detection, the anchor boxes of our training data set are reclustered for replacing some of the original you only look once v3 prior anchor boxes that are not appropriate to our data set. By virtue of the proposed detection method, the capture robot has the capability of cleaning floating garbage in the field. Experimental results demonstrate that both detection speed and accuracy of the modified you only look once v3 are better than those of other object detection algorithms. The obtained results provide valuable insight into the high-speed detection and grasping of dynamic objects in complex aquatic environments autonomously and intelligently.

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Development of an Autonomous Unmanned Aerial Manipulator Based on a Real-Time Oriented-Object Detection Method

Sensors ◽

10.3390/s19102396 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2396 ◽

Cited By ~ 1

Author(s):

Shijie Lin ◽

Jinwang Wang ◽

Rui Peng ◽

Wen Yang

Keyword(s):

Object Detection ◽

Real Time ◽

Detection Method ◽

Positioning Control ◽

3 Dimensional ◽

Aerial Manipulator ◽

Global Positioning ◽

Experimental Trials ◽

Open Source Hardware ◽

Oriented Object

Autonomous Unmanned Aerial Manipulators (UAMs) have shown promising potential in mobile 3-dimensional grasping applications, but they still suffer from some difficulties impeding their board applications, such as target detection and indoor positioning. For the autonomous grasping mission, the UAMs need ability to recognize the objects and grasp them. Considering the efficiency and precision, we present a novel oriented-object detection method called Rotation-SqueezeDet. This method can run on embedded-platforms in near real-time. Besides, this method can give the oriented bounding box of an object in images to enable a rotation-aware grasping. Based on this method, a UAM platform was designed and built. We have given the formulation, positioning, control, and planning of the whole UAM system. All the mechanical designs are fully provided as open-source hardware for reuse by the community. Finally, the effectiveness of the proposed scheme was validated in multiple experimental trials, highlighting its applicability of autonomous aerial rotational grasping in Global Positioning System (GPS) denied environments. We believe this system can be deployed to many potential workplaces which need UAM to accomplish difficult manipulation tasks.

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An Edge Computing-Enabled Train Obstacle Detection Method Based on YOLOv3

Wireless Communications and Mobile Computing ◽

10.1155/2021/7670724 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Song Li ◽

Hongli Zhao ◽

Jinmin Ma

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Detection Method ◽

Obstacle Detection ◽

Edge Computing ◽

Traffic Load ◽

Learning Tasks ◽

Onboard Equipment

Rail transit is developing towards intelligence which takes lots of computation resource to perform deep learning tasks. Among these tasks, object detection is the most widely used, like track obstacle detection, catenary wear, and defect detection and looseness detection of train wheel bolts. But the limited computation capability of the train onboard equipment prevents running deep and complex detection networks. The limited computation capability of the train onboard equipment prevents conducting complex deep learning tasks. Cloud computing is widely utilized to make up for the insufficient onboard computation capability. However, the traditional cloud computing architecture will bring in uncertain heavy traffic load and cause high transmission delay, which makes it fail to complete real-time computing intensive tasks. As an extension of cloud computing, edge computing (EC) can reduce the pressure of cloud nodes by offloading workloads to edge nodes. In this paper, we propose an edge computing-based method. The onboard equipment on a fast-moving train is responsible for acquiring real-time images and completing a small part of the inference task. Edge computing is used to help execute the object detection algorithm on the trackside and carry most of the computing power. YOLOv3 is selected as the object detection model, since it can balance between the real-time and accurate performance on object detection compared with two-stage models. To save onboard equipment computation resources and realize the edge-train cooperative interface, we propose a model segmentation method based on the existing YOLOv3 model. We implement the cooperative inference scheme in real experiments and find that the proposed EC-based object detection method can accomplish real-time object detection tasks with little onboard computation resources.

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Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments

IEEE Access ◽

10.1109/access.2019.2961959 ◽

2020 ◽

Vol 8 ◽

pp. 1935-1944 ◽

Cited By ~ 7

Author(s):

Wei Fang ◽

Lin Wang ◽

Peiming Ren

Keyword(s):

Object Detection ◽

Real Time ◽

Detection Method ◽

Constrained Environments

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Motion Multi-object Detection Method under Complex Environment

2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) ◽

10.1109/iciicii.2016.0032 ◽

2016 ◽

Author(s):

Zi-Xiao Pan ◽

Mei Wang

Keyword(s):

Object Detection ◽

Detection Method ◽

Complex Environment

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Deteksi Sampah pada Real-time Video Menggunakan Metode Faster R-CNN

Applied Technology and Computing Science Journal ◽

10.33086/atcsj.v3i2.1846 ◽

2021 ◽

Vol 3 (2) ◽

pp. 117-125

Author(s):

M Fadhilur Rahman ◽

Bambang Bambang

Keyword(s):

Object Detection ◽

Real Time ◽

Detection Method ◽

Human Life ◽

Video Frame ◽

Test Results

Garbage is a never-ending problem in human life. Many of the problems caused by waste actually stem from human ignorance of the environment. Several solutions have been proposed to solve and avoid problems from the waste, one of which is making waste detection that can be applied directly to certain devices. This study aims to apply an object detection method in the form of Faster R-CNN to detect and classify at a speed that allows computers to detect trash objects directly through real-time video. The test results in this study indicate the method used can detect trash objects in 100 images with an accuracy value of 74%, and to detect real-time video with video frame rates in the range of 1 frame per second (fps).

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Real-time Vehicle Object Detection Method Based on Multi-scale Feature Fusion

IEEE Access ◽

10.1109/access.2021.3104849 ◽

2021 ◽

pp. 1-1

Author(s):

Keyou Guo ◽

Xue Li ◽

Mo Zhang ◽

Qichao Bao ◽

Min Yang

Keyword(s):

Object Detection ◽

Real Time ◽

Detection Method ◽

Feature Fusion ◽

Scale Feature ◽

Multi Scale

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Object Detection Method Based on YOLOv3 using Deep Learning Networks

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4121.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1414-1417

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Real World ◽

Detection Method ◽

Cross Entropy ◽

Detection Methods ◽

Learning Networks ◽

Learning Techniques ◽

Localization Errors

—Object Detection is being widely used in the industry right now. It is the method of detection and shaping real-world objects. Even though there exist many detection methods, the accuracy, rapidity, and efficiency of detection are not good enough. So, this paper demonstrates real-time detection using the YOLOv3 algorithm by deep learning techniques. It first makes expectations crosswise over 3 unique scales. The identification layer is utilized to make recognition at highlight maps of three distinct sizes, having strides 32, 16, 8 individually. This implies, with partner contribution of 416 x 416, we will in general form location on scales 13 x 13, 26 x 26 and 52x 52. Meanwhile, it also makes use of strategic relapse to anticipate the jumping box article score, the paired cross-entropy misfortune is utilized to foresee the classes that the bounding box may contain, the certainty is determined and afterward the forecast. It results in perform multi-label classification for objects detected in images, the average preciseness for tiny objects improved, it's higher than quicker RCNN. MAP increased significantly. As MAP increased localization errors decreased.

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Development and Application of Quality Control System for Highway Subgrade Construction

智能城市应用 ◽

10.33142/sca.v2i5.814 ◽

2019 ◽

Vol 2 (5) ◽

Author(s):

Tong Wang

Keyword(s):

Quality Control ◽

Real Time ◽

Detection Method ◽

System Development ◽

Effective Control ◽

Construction Process ◽

Development Mode ◽

The Road ◽

Whole Process

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

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