MR-i, high speed hyperspectral imaging spectroradiometer

Author(s):  
Florent Prel ◽  
Louis Moreau ◽  
Stephane Lantagne ◽  
Christian Vallières ◽  
Claude Roy ◽  
...  
Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim

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.


2011 ◽  
Author(s):  
Florent Prel ◽  
Louis Moreau ◽  
Stéphane Lantagne ◽  
Claude Roy ◽  
Christian Vallières ◽  
...  

2012 ◽  
Vol 102 (3) ◽  
pp. 581a
Author(s):  
Patrick J. Cutler ◽  
Michael D. Malik ◽  
Sheng Liu ◽  
Jason M. Byars ◽  
Diane S. Lidke ◽  
...  

Author(s):  
András Jung ◽  
Matthias Locherer ◽  
René Heine

2017 ◽  
Author(s):  
David B. Kelley ◽  
Anish K. Goyal ◽  
Ninghui Zhu ◽  
Derek A. Wood ◽  
Travis R. Myers ◽  
...  

2021 ◽  
Author(s):  
Jongchan Park ◽  
Xiaohua Feng ◽  
Rongguang Liang ◽  
Liang Gao

2020 ◽  
Vol 10 (18) ◽  
pp. 6569
Author(s):  
Mohammad Akbar Faqeerzada ◽  
Mukasa Perez ◽  
Santosh Lohumi ◽  
Hoonsoo Lee ◽  
Geonwoo Kim ◽  
...  

Almonds are nutrient-rich nuts. Due to their high level of consumption and relatively high price, their production is targeted for illegal practices, with the intention of earning more profit. The most common adulterants are based on superficial matching, and as an adulterant, the apricot kernel is comparatively inexpensive and almost identical in color, texture, odor, and other physicochemical characteristics to almonds. In this study, a near-infrared hyperspectral imaging (NIR-HSI) system in the wavelength range of 900–1700 nm synchronized with a conveyor belt was used for the online detection of added apricot kernels in almonds. A total of 448 samples from different varieties of almonds and apricot kernels (112 × 4) were scanned while the samples moved on the conveyor belt. The spectral data were extracted from each imaged nut and used to develop a partial least square discrimination analysis (PLS-DA) model coupled with different preprocessing techniques. The PLS-DA model displayed over a 97% accuracy for the validation set. Additionally, the beta coefficient obtained from the developed model was used for pixel-based classification. An image processing algorithm was developed for the chemical mapping of almonds and apricot kernels. Consequently, the obtained model was transferred for the online sorting of seeds. The online classification system feedback had an overall accuracy of 85% for the classification of nuts. However, the model presented a relatively low accuracy when evaluated in real-time for online application, which might be due to the rough distribution of samples on the conveyor belt, high speed, delaying time in suction, and lighting variations. Nevertheless, the developed online prototype (NIR-HSI) system combined with multivariate analysis exhibits strong potential for the classification of adulterated almonds, and the results indicate that the system can be effectively used for the high-throughput screening of adulterated almond nuts in an industrial environment.


2016 ◽  
Vol 75 ◽  
pp. 173-179 ◽  
Author(s):  
Ashabahebwa Ambrose ◽  
Lalit Mohan Kandpal ◽  
Moon S. Kim ◽  
Wang-Hee Lee ◽  
Byoung-Kwan Cho

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