scholarly journals Automatic sorting of recycled aggregate using image processing and object detection

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Farid Sartipi ◽  
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
Recycled Aggregate ◽  
Automatic Sorting

scholarly journals Object Detection Robot Using Fuzzy Logic Controller Through Image Processing

2021 ◽  
Vol 1737 (1) ◽  
pp. 012045
Author(s):  
M Khairudin ◽  
S Yatmono ◽  
AC Nugraha ◽  
M Ikhsani ◽  
A Shah ◽  
...  
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Object Detection ◽  
Fuzzy Logic Controller

scholarly journals A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test

2020 ◽  
Vol 10 (14) ◽  
pp. 4744
Author(s):  
Hyukzae Lee ◽  
Jonghee Kim ◽  
Chanho Jung ◽  
Yongchan Park ◽  
Woong Park ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
High Speed ◽  
Detection Algorithm ◽  
Learning Technologies ◽  
Experimental Conditions ◽  
Fragmentation Test ◽  
Detection Approach ◽  
Previous Image

The arena fragmentation test (AFT) is one of the tests used to design an effective warhead. Conventionally, complex and expensive measuring equipment is used for testing a warhead and measuring important factors such as the size, velocity, and the spatial distribution of fragments where the fragments penetrate steel target plates. In this paper, instead of using specific sensors and equipment, we proposed the use of a deep learning-based object detection algorithm to detect fragments in the AFT. To this end, we acquired many high-speed videos and built an AFT image dataset with bounding boxes of warhead fragments. Our method fine-tuned an existing object detection network named the Faster R-convolutional neural network (CNN) on this dataset with modification of the network’s anchor boxes. We also employed a novel temporal filtering method, which was demonstrated as an effective non-fragment filtering scheme in our recent previous image processing-based fragment detection approach, to capture only the first penetrating fragments from all detected fragments. We showed that the performance of the proposed method was comparable to that of a sensor-based system under the same experimental conditions. We also demonstrated that the use of deep learning technologies in the task of AFT significantly enhanced the performance via a quantitative comparison between our proposed method and our recent previous image processing-based method. In other words, our proposed method outperformed the previous image processing-based method. The proposed method produced outstanding results in terms of finding the exact fragment positions.

scholarly journals Optical edge detection based on high-efficiency dielectric metasurface

2019 ◽  
Vol 116 (23) ◽  
pp. 11137-11140 ◽  
Author(s):  
Junxiao Zhou ◽  
Haoliang Qian ◽  
Ching-Fu Chen ◽  
Junxiang Zhao ◽  
Guangru Li ◽  
...  
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Object Detection ◽  
High Efficiency ◽  
Berry Phase ◽  
Detection Mechanism ◽  
Optical Efficiency ◽  
Optical Components ◽  
Experimental Realization ◽  
Smart Cameras

Optical edge detection is a useful method for characterizing boundaries, which is also in the forefront of image processing for object detection. As the field of metamaterials and metasurface is growing fast in an effort to miniaturize optical devices at unprecedented scales, experimental realization of optical edge detection with metamaterials remains a challenge and lags behind theoretical proposals. Here, we propose a mechanism of edge detection based on a Pancharatnam–Berry-phase metasurface. We experimentally demonstrated broadband edge detection using designed dielectric metasurfaces with high optical efficiency. The metasurfaces were fabricated by scanning a focused laser beam inside glass substrate and can be easily integrated with traditional optical components. The proposed edge-detection mechanism may find important applications in image processing, high-contrast microscopy, and real-time object detection on compact optical platforms such as mobile phones and smart cameras.

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