智能垃圾桶_智能垃圾桶csdn

智能垃圾桶

背景

随着人们对环境保护意识的不断提高，垃圾分类已经成为了全球范围内的一个热门话题。为了提高垃圾分类的效率和准确性，我们开发了一款基于单片机的智能垃圾桶，它能够自动识别垃圾种类并进行分类。

原理

智能垃圾桶的原理是通过内置的传感器和单片机控制系统，识别垃圾种类并进行分类。我们采用了图像处理技术和机器学习算法，使用OpenCV和TensorFlow框架构建了一个卷积神经网络（CNN）模型。该模型通过训练数据集，学习垃圾图像的特征，从而能够准确地识别垃圾种类。

代码

以下是我们使用的Arduino代码，用于控制智能垃圾桶的传感器和执行分类操作：

#include <Servo.h>
#include <SoftwareSerial.h>
#include <Wire.h>
#include <Adafruit_SSD1306.h>
#include <Adafruit_GFX.h>
#include <TensorFlowLite.h>
#include <tensorflow/lite/experimental/micro/kernels/all_ops_resolver.h>
#include <tensorflow/lite/experimental/micro/micro_error_reporter.h>
#include <tensorflow/lite/experimental/micro/micro_interpreter.h>
#include <tensorflow/lite/schema/schema_generated.h>
#include <tensorflow/lite/version.h>

#define OLED_RESET 4
Adafruit_SSD1306 display(OLED_RESET);

#define SERVO_PIN 5
Servo myservo;

#define TRIGGER_PIN 6
#define ECHO_PIN 7
#define MAX_DISTANCE 200

SoftwareSerial mySerial(8, 9); // RX, TX

const int kNumCols = 128;
const int kNumRows = 128;
const int kNumChannels = 3;

const int kTensorArenaSize = 60 * 1024;
uint8_t tensor_arena[kTensorArenaSize];

const char* labels[] = {"cardboard", "glass", "metal", "paper", "plastic", "trash"};

const tflite::Model* model = tflite::GetModel(garbage_classification_tflite);
tflite::MicroErrorReporter micro_error_reporter;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* input = nullptr;
TfLiteTensor* output = nullptr;

void setup() {
  Serial.begin(9600);
  myservo.attach(SERVO_PIN);
  pinMode(TRIGGER_PIN, OUTPUT);
  pinMode(ECHO_PIN, INPUT);
  display.begin(SSD1306_SWITCHCAPVCC, 0x3C);
  display.clearDisplay();
  display.display();
  mySerial.begin(9600);

  static tflite::MicroOpResolver<6> micro_op_resolver;
  micro_op_resolver.AddConv2D();
  micro_op_resolver.AddMaxPool2D();
  micro_op_resolver.AddReshape();
  micro_op_resolver.AddFullyConnected();
  micro_op_resolver.AddSoftmax();
  micro_op_resolver.AddQuantize();

  static tflite::MicroInterpreter static_interpreter(
      model, micro_op_resolver, tensor_arena, kTensorArenaSize, &micro_error_reporter);
  interpreter = &static_interpreter;

  interpreter->AllocateTensors();

  input = interpreter->input(0);
  output = interpreter->output(0);
}

void loop() {
  long duration, distance;
  digitalWrite(TRIGGER_PIN, LOW);  
  delayMicroseconds(2); 
  digitalWrite(TRIGGER_PIN, HIGH);
  delayMicroseconds(10); 
  digitalWrite(TRIGGER_PIN, LOW);
  duration = pulseIn(ECHO_PIN, HIGH);
  distance = duration / 58;

  if (distance <= MAX_DISTANCE) {
    myservo.write(90);
    delay(500);

    uint8_t image[kNumCols * kNumRows * kNumChannels];
    for (int i = 0; i < kNumCols * kNumRows * kNumChannels; i++) {
      image[i] = mySerial.read();
    }

    for (int i = 0; i < kNumCols * kNumRows * kNumChannels; i++) {
      input->data.int8[i] = (image[i] - 128) / 128.0f;
    }

    TfLiteStatus invoke_status = interpreter->Invoke();
    if (invoke_status != kTfLiteOk) {
      Serial.println("Invoke failed!");
      return;
    }

    int max_index = 0;
    float max_value = -1;
    for (int i = 0; i < 6; i++) {
      float value = output->data.f[i];
      if (value > max_value) {
        max_value = value;
        max_index = i;
      }
    }

    display.clearDisplay();
    display.setTextSize(1);
    display.setTextColor(WHITE);
    display.setCursor(0, 0);
    display.println("Detected:");
    display.println(labels[max_index]);
    display.display();
  } else {
    myservo.write(0);
  }
}
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总结

基于单片机的智能垃圾桶，通过使用图像处理技术和机器学习算法，实现了对垃圾种类的自动识别和分类。该项目的实现过程中，我们使用了Arduino开发板和TensorFlowLite框架，构建了一个卷积神经网络模型，通过传感器获取垃圾图像，并将其输入到模型中进行分类。未来，基于单片机的智能垃圾桶的应用前景将会越来越广阔，为城市环境保护事业做出更大的贡献。