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--- iothings:proiecte:2022:bear_detection_system [2023/01/20 04:35]
aconstantinescu1108 [6.Resources]
+++ iothings:proiecte:2022:bear_detection_system [2023/02/11 22:14] (current)
aconstantinescu1108 [Bear Detection System]
@@ Line 1: / Line 1: @@
 ====== Bear Detection System ======
-Constantinescu Alexandru-Iulian - ACES
+Alexandru-Iulian Constantinescu - ACES
 aconstantinescu1108@stud.etti.upb.ro
+[[https://github.com/constAlexandru/IoT_project_ACES2]]
 ==== 1.Description ====
@@ Line 72: / Line 74: @@
 Afterwards I uploaded the header from the ArduinoIDE sketch.
-<note tip>In the header file, changing the array of weights to be //const// moved the array the Flash memory, making it easier to fit the model on the board</note>
+<note tip>In the header file, changing the array of weights to be //const// moved the array in the Flash memory, making it easier to fit the model on the board</note>
 === 3.2. Firebase Storage ===
@@ Line 93: / Line 95: @@
 ==== 4.Results ====
-Overall the implementation of the system is not done. I made the neural network for detecting bears, and put it on the ESP32-CAM. The ultrasonic sensor also works well. The problems I am currently facing are connecting to the Firebase and to the SMTP server. I was able to connect to Firebase with the current ESP32-CAM and upload images unitil recently, as can be seen in the image from chapter 3.2. One challenge I encountered on working on this project were the faulty ESP32-CAMs (one arrived broken and 2 became broken after a few hours of usage). As I can't see exactly the images taken is also hard to adapt the neural network for correct inference on the board.
+Overall the implementation of the system is not done. I made the neural network for detecting bears, and put it on the ESP32-CAM. The ultrasonic sensor also works well. The problems I am currently facing are connecting to the Firebase and to the SMTP server. I was able to connect to Firebase with the current ESP32-CAM and upload images unitil recently, as can be seen in the image from chapter 3.2. One challenge I encountered on working on this project were the faulty ESP32-CAMs (one arrived broken and 2 became broken after a few hours of use). As I can't see exactly the images taken is also hard to adapt the neural network for correct inference on the board.
 ==== 5.Conclusions ====
@@ Line 102: / Line 104: @@
 ==== 6.Resources ====
 Besides the links from above, I would like to mention:
 [[https://www.tensorflow.org/lite/microcontrollers/get_started_low_level]]
 [[https://www.digikey.com/en/maker/projects/intro-to-tinyml-part-2-deploying-a-tensorflow-lite-model-to-arduino/59bf2d67256f4b40900a3fa670c14330]]
 [[https://www.youtube.com/watch?v=Qu-1RK4Fk7g&ab_channel=AndreasSpiess]]
 [[https://netron.app/]]
 [[https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/person_detection]]
 [[https://randomnerdtutorials.com/esp32-cam-ov2640-camera-settings/]]