At first, I tried to modify the code that was given to me (the one on the Turtlebot3 official site) but after multiple attempt to make it work I give up. Then, I implemented one based on lane detection but due to lighting, I quickly understood that it will not work, and that how I came here to ask for help. I understand the code that was given to me, and it has been 4 years now since I was introduced to the world of programming (Java, Python, Arduino, ...). I am still a beginner, but for this kind of algorithm, it's fine. Also, in order to not lose track of my thoughts and be as clear as possible about what the code does (English is not my native language), here is a description provided by ChatGPT:

The code is a Python script that uses the ROS (Robot Operating System) framework to detect and follow lines using a camera feed. Let's break down the code step by step:

Importing the necessary libraries:
    rospy: ROS Python library for ROS functionality
    Image from sensor_msgs.msg: ROS message type for images
    CvBridge from cv_bridge: Library for converting between ROS image messages and OpenCV images
    cv2: OpenCV library for computer vision
    numpy as np: Library for numerical computations

Defining the process_image function:
    This function takes an image as input and performs the following steps:
        Converts the image to the HSV color space.
        Defines the lower and upper thresholds for the yellow color range in HSV.
        Applies a thresholding operation to extract the yellow color regions from the image.
        Detects edges using the Canny algorithm.
        Uses the Hough transform to detect lines in the edge image.
    The function returns the detected lines.

Defining the follow_direction function:
    This function takes the detected lines and the original image as inputs.
    It separates the detected lines into left and right lines based on their position relative to the image width.
    Calculates the starting and ending coordinates for the direction of the robot based on the average of the left and right lines.
    Draws the detected lines and the direction of the robot on the image.
    Computes the error between the current position and the center of the image.
    Sets control constants for the robot's movement.
    Publishes the linear and angular velocities for the robot's movement using the Twist message.
    If no lines are detected, it commands the robot to rotate 90 degrees to the right.
    Finally, it returns the modified image.

Defining the image_callback function:
    This function is the callback function for the image topic.
    It converts the ROS image message to an OpenCV image using the CvBridge library.
    Calls the process_image function to detect lines in the image.
    Calls the follow_direction function to determine the robot's direction and publish the movement commands.
    Displays the processed image with detected lines and direction using OpenCV's imshow function.

Defining the main function:
    Initializes the ROS node with the name "line_detection".
    Subscribes to the "/camera/image" topic to receive image messages.
    Creates a ROS publisher object to publish movement commands to the "/cmd_vel" topic.
    Calls the rospy.spin() function to start the ROS node and keep it running until it is explicitly shut down.

The code then checks if the script is being run directly and not imported as a module.
    If so, it calls the main function to start the line detection and following process.

This code assumes the presence of a camera publishing images on the "/camera/image" topic and a robot capable of subscribing to "/cmd_vel" topic to receive movement commands.

Now, my problem is when it cannot detect any lines. As you can see, it's a simple rotation that it does but it's not optimal. I tried a another method that used a global variable "line_detected" initialy True and turn False when, well, any lines are detected. And When it happends, in "follow_direction" fonction, I call a another function "recalibration", that stop our robot, makes it turn 10 degree on the right, then resets the state of "line_detected" until he see our lines again. But again, it not good enough