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--- iothings:proiecte:2022:bear_detection_system [2023/01/20 03:45]
aconstantinescu1108 [3.Software]
+++ 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 92: / Line 94: @@
 ==== 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 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 ====
+  * Even with augmentation, it's still preferable to have a bigger dataset for training neural networks
+  * Neural networks have become smaller (some of them) but they need to become more small in order to easily fit in small embedded systems
+  * IoT projects can be quite complex as they involve many aspects from communication to memory management, power management and sometimes even neural networks but with enough creativity their applications seem endless
+==== 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/]]