scholarly journals Object Color Estimation with Dominant Color in Automated Image Semantic Description Generation Tasks

2019 ◽  
pp. 90-97
Author(s):  
Aghasi Poghosyan
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection System ◽  
Time Algorithm ◽  
Semantic Description ◽  
Image Tagging ◽  
Dominant Color ◽  
Color Name ◽  
Detection And Localization ◽  
Localization Systems

The automated image tagging is an important part of modern search engines. The generated image tags can be constructed from object names and their attributes, for example, colors. This work presents an object color name detection real-time algorithm. It is applicable to any automatic object detection and localization systems. The presented algorithm is fast enough to run after the existing real-time object detection system, without adding visible overhead. The algorithm uses k-means to detect the dominant color and selects the correct name for the color via Delta E (CIE 2000).

scholarly journals Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hyperspectral Imaging ◽  
High Speed ◽  
Detection System ◽  
Imaging Techniques ◽  
Foreign Object ◽  
Subtraction Method ◽  
Foreign Objects ◽  
Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

scholarly journals Real Time Object Detection Canbe Embedded on Low Powered Devices

2019 ◽  
Author(s):  
Jimut Bahan Pal
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Open Source ◽  
Real Time ◽  
Detection System ◽  
Computing Power ◽  
Convolution Neural Networks ◽  
Real Challenge ◽  
New Algorithms

It has been a real challenge for computers with low computing power and memory to detect objects in real time. After the invention of Convolution Neural Networks (CNN) it is easy for computers to detect images and recognize them. There are several technologies and models which can detect objects in real time, but most of them require high end technologies in terms of GPUs and TPUs. Though, recently many new algorithms and models have been proposed, which runs on low resources. In this paper we studied MobileNets to detect objects using webcam to successfully build a real time objectdetection system. We observed the pre trained model of the famous MS COCO dataset to achieve our purpose. Moreover, we applied Google’s open source TensorFlow as our back end. This real time object detection system may help in future to solve various complex vision problems.

Robotic Monitoring of Human and Animal-Real Time and Automated

2020 ◽  
Vol 17 (2) ◽  
pp. 803-813
Author(s):  
Emy Haryatmi ◽  
Aris Tito Sugihharto ◽  
Maulana Mujahidin
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Short Message Service ◽  
Detection System ◽  
Ultrasonic Sensor ◽  
Short Message ◽  
Message Service ◽  
Sms Notification

Autonomous robotic monitoring of human and animal used to secure the house or the flat. This robot can move freely from one side to another side of the house or the flat. Accordingly, this robot is sending the vision to android smartphone in real time. Therefore, the owner of the house or the flat can see inside the house while they were not around. The objective of this research is to develop autonomous robotic to be able to monitor the area from the existing of human and animal and send the vision to android smartphone in real time. This research used four leg robotic as an autonomous robotic monitoring. Four leg robotic is equipped with camera which connected to router based on wireless connection, PIR and ultrasonic sensor and GSM module to send short message service (SMS) notification if PIR sensor detect some movement. The experiment used cat as an animal object. Experiment showed that this robot can follow the object, either a human or an animal, within range of 10–45 cm from robot in 12 seconds and send the vision to android smartphone in real-time. The SMS is received in 5 seconds after camera capture the object. This robotic cannot determine who is the object on the camera because it is not equipped with object detection system.

Real-time object detection system on PAC Duo platform

2010 ◽  
Author(s):  
Chao-Yi Cho ◽  
Jen-Kuei Yang ◽  
Shau-Yin Tseng ◽  
I-Hsien Lee ◽  
Ming-Hwa Sheu
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection System

scholarly journals Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility

2021 ◽  
Vol 7 (8) ◽  
pp. 145
Author(s):  
Antoine Mauri ◽  
Redouane Khemmar ◽  
Benoit Decoux ◽  
Madjid Haddad ◽  
Rémi Boutteau
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Video Game ◽  
Autonomous Vehicles ◽  
Object Localization ◽  
Driver Assistance Systems ◽  
Smart Mobility ◽  
Bounding Boxes ◽  
Detection And Localization

For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localization, object dimensions, and object orientation. Our method has been evaluated on KITTI’s road dataset as well as on our own hybrid virtual road/rail dataset acquired from the video game Grand Theft Auto (GTA) V. The evaluation of our method on these two datasets shows good accuracy, but more importantly that it can be used in real-time conditions, in road and rail traffic environments. Through our experimental results, we also show the importance of the accuracy of prediction of the regions of interest (RoIs) used in the estimation of 3D bounding box parameters.

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