Development of a Deep-Learning Pipeline to Detect and Locate Contaminants of Industrial Products via non-Invasive Microwave Signals-Reference-Cited by-同舟云学术

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Development of a Deep-Learning Pipeline to Detect and Locate Contaminants of Industrial Products via non-Invasive Microwave Signals

Published:2023-09-25 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Conference on AgriFood Electronics (CAFE)
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Author:

Musumeci Margherita¹,Amaya Cano Juan Sebastian²,Lazzati Filippo¹,Martano Chiara³,Pappone Francesco⁴,Ramonda Claudio³,Ricci Marco⁵,Tobon Jorge A.⁶,Turvani Giovanna⁶,Casu Mario R.⁶,Mussetta Marco⁷,Vipiana Francesca⁶

Affiliation:

1. Politecnico di Milano,Dept. of Electronics, Information and Bioengineering,Milan,Italy

2. Politecnico di Milano,Dept. of Chemistry, Materials and Chemical Engineering,Milan,Italy

3. Politecnico di Torino,Dept. of Management and Production Engineering,Turin,Italy

4. Politecnico di Torino,Dept. of Mathematical Sciences,Turin,Italy

5. Wavision Srl, Antares Vision group,Turin,Italy

6. Politecnico di Torino,Dept. of Electronics and Telecommunications,Turin,Italy

7. Politecnico di Milano,Dept. of Energy,Milan,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10291171/10291197/10291829.pdf?arnumber=10291829

Reference19 articles.

1. Real-time detection of plastic shards in cheese using microwave-sensing technique;muradov;Proceedings,2020

2. Microwave Sensing for Food Safety: a Neural Network Implementation

3. Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles

4. UWB-sensors in food quality management — the way from the concept to market

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