Drone is designed to inspect whether the rule of wearing the facemask is practiced strictly or not in crowded place and to predict ripening stage of banana.

1. QUADCOPTER FOR MONITORING AND DETECTION
(SOFTWARE)
B.E Stage II Project Presentation Submitted in fulfillment of the requirements of the degree of
Bachelor of Engineering
By
Under the guidance of
Dr. Pandharinath Ghonge
Department of Electronics & Telecommunication Engineering
St. John College of Engineering and Management
Mumbai University
2020-2021
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TEJAS A. DALVI EU1173011
SHIVAM R. SHARMA EU1173014
MOHINI D. DHAWALE EU1173021

2. Presentation Flow
• Problem Statement
• Literature Review
• Block Diagram
• Components
• Flowchart
• Results
• Paper Publishment
• References
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3. Problem Statement
To develop Machine Learning Models using Tensorflow, Keras
and OpenCV that will serve the task of easing the conventional
methods followed for the circumstances enlisted below:
• Detecting mask’s of people in a crowded situation and, to
inspect whether the rule of wearing the mask is practiced
strictly or not.
• Predicting the ripening condition of a Banana.
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4. 4

5. IoT-based System for COVID-19 Indoor SafetyMonitoring
For Face Mask Detection Algorithm three libraries are used in this project:
haarcascade_frontalface_default, haarcascade_mcs_mouth and
haarcascade_mcs_nose to detect human face , human mouth and nose with
provided frame.
Advantage:
• Using Raspberry pi3, accuracy was 84-91%.
Disadvantages:
• Despite the acceptable accuracy of mask detection algorithm, it is not
designed to detect transparent masks and face shields
• Frame rate was 0.76/2.83 fps
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6. COVID-19 Face Mask Detection With Deep Learning and Computer
Vision
The proposed system uses a transfer learning approach to performance
optimization with a deep learning algorithm and a computer vision to
automatically monitor people in public places with a camera integrated with a
raspberry pi4 and to detect people with mask or no mask.
Advantages:
• outperforming Faster than R-CNN model
• The system detects the social distancing and masks with a precision score of
91.7% with confidence score 0.7, precision value 0.91 and the recall value
0.91 with FPS = 28.07.
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7. Face Mask Detection at the Fog Computing Gateway
The system used two binary classifier based on MobileNetV2. Classifier-1 is
trained with Data-set which contain 770 facial images which is of classes mask
and without mask.Classifier-2 is also trained with data-sets which contains
images with classes proper and improper mask wear.
Advantages:
• Accuracy for model1 was ranging from 0.75 - 0.83
• Accuracy for model2 was ranging from 0.8 - 0.92
Disadvantages:
• fluctuations in the accuracy and loss values due to model over fitting
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8. Development of an Effective System for Remote Monitoring of
Banana Ripening Process
In This Paper Author has completed an easy monitoring system to monitor the color indices of
the bananas and to identify ripening stage of banana. Bascom code has been used for the
programming Of the Microcontroller and AVR dude has been used To burn the hex file into the
microcontroller. The MATLAB software is responsible to take the image Periodically and to
identify the ripening stage of the Bananas.
Advantages
• System has been working effectively as 93% On find the colors.
• Temperature and ripening stage Directly sent to the monitoring person mobile number via
GSM module.
Disadvantages
• microcontroller ATMEGA16 due to this Interfacing with high-power devices cannot possible
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9. Design and development of a portable instrument for the detection
of artificial ripening of banana fruit
The given paper proposes a conceptual method to detect artificially ripened banana fruit using
image processing technique. Thus they had design and development of such device to detect
whether given banana is artificially or naturally ripened. The device consists of banana holder
mechanism along with motor for its rotation as well as the inbuilt camera to capture the surface
image of placed banana.
Advantages
• Only the portion of banana image to be captured, this reduceses the memory required for
image storing and processing.
Disadvantages
• They used CMYK that Doesn’t shift well from RGB.
• Cannot properly display all colors.
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10. Multi-spectral imaging for artificial ripened banana Detection
In This Paper Author has completed multi-spectral imaging in eight narrow
spectrum bands across VIS and NIR range to detect the artificial ripened
banana. To present the qualitative and quantitative analysis on the sample size
of 5760 images, they has been repeated the experiment for 10 different
iterations of randomly selected training and testing banana samples such that
the final average classification accuracy can be computed.
Advantages
• The obtained average classification accuracy of 97.5%.
• BSIF-SVM, LG-SVM, and LPQ-SVM have consistently Indicated the highest
average classification accuracy.
Disadvantages
• VIS spectrometer is the time it takes to prepare to use one.
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11. Block Diagram
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12. 12

13. Face Mask Detection
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14. Banana Ripening
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16. Results
After summing up all the research done in development phase we conclude that
the, ‘Quadcopter for Monitoring and Detection,’ stands for an efficient
surveillance-cum-analyzer, which would definitely stand on its terms; for the
applications assigned to it.
Which are:
1.Face mask detection.
2. Ripening of Banana.
It provides a wholesome environment which is driven for reducing the excess
labour work and risk bagged with it. GUI provides a perfect man- machine
interface which will be easily adaptable to the user to understand the
technology. It tends to make its presence where, humans are unreachable until
and unless backed up with some equipments.
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17. Facemask Detection Results
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18. Banana Ripening Results
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19. Firebase Online Database
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20. Future Scope
• High Resolution Camera
• Hardware Upgradation
• Replacing Dongle with LORA Module
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21. Paper Publishment
• Shivam Sharma, Tejas Dalvi, Mohini Dhawale,
Dr. Pandharinath Ghonge, “Face Mask Detection using
MobileNetV2”, Submitted at International Journal for
Research in Applied Science and Engineering Technology
(IJRASET) Research Publication, Volume 9 Issue V May
2021
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22. References
• Nenad Petrović, Đorđe Kocić, “IoT-based System for COVID-19 Indoor SafetyMonitoring”, 2020.
• Vinitha.V, Velantina.V, “COVID-19 Face Mask Detection With Deep Learning and Computer Vision”,
Department of Master of Computer Application, AMC Engineering College Bangalore, August 2020.
• Srinivasa Raju Rudraraju, Nagender Kumar Suryadevara, Atul Negi, “Face Mask Detection at the Fog
Computing Gateway”, School of Computer and Information Sciences University of Hyderabad, 2020.
• Amit Verma, Rajendra Hegadi, Kamini Sahu, "Development of an Effective System for Remote
Monitoring of Banana Ripening Process", Department of ET&T, KITE Raipur, CG, India, December
2015.
• Veena Hallur, Bhagyashree Atharga, Amruta Hosur, Bhagyashree Binjawadagi, K. Bhat, "Design and
development of a portable instrument for the detection of artificial ripening of banana fruit", Students,
Department of E&CE, Basaveshwar Engineering College, Bagalkot, Karnataka, India, November
2014.
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23. References
• Narayan Vetrekar, Raghavendra Ramachandra, Kiran B. Raja, R. S. Gad, "Multi-spectral imaging for
artificial ripened banana Detection", Department of Electronics, Goa University, Taleigao Plateau, Goa,
India, 2019.
• Ahmed AboBakr, Menna Mohsen, Lobna A. Said, Ahmed H. Madian, Ahmed S. Elwakil, and Ahmed G.
Radwan, "Banana Ripening and Corresponding Variations in Bio-Impedance and Glucose Levels",
NISC Research Center, Nile University, Cairo, Egypt,2019.
• Darvin M. Taghoy, Jocelyn Flores Villaverde, "A Fuzzy Logic Approach for the Determination of
Cavendish Banana Shelf Life " School of Electrical, Electronics and Computer Engineering, Mapúa
University Manila, Philippines, October 2018.
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24. Thank you!
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