Automating Machinery with Object Detection using YOLO and Servo Controllers

  • Riya Peter
  • Gillian Pereira
  • Yash Kamble
  • M. B. Wagh


Now-a-days Computer Vision and Machine Learning algorithms play an important role in automation. With the help of Computer Vision and deep learning algorithms, data like images and videos are being used for classification and prediction. This paper proposes a real time object detector using computer vision and deep learning algorithms. YOLO (You Only Look Once) which is a deep learning algorithm is a state-of-the-art algorithm used for object detection.  A binary classifier using CNN (Convolutional Neural Network) can be used to detect whether a class is present or absent indicating the presence or absence of a particular object. The two classes will be A) desired object present and B) the desired object absent. The input to the model will be from a live camera. The classifier detects the object by creating a bounding box around the object and then predicting the class. If class A is predicted the model will calculate the distance from the object.  After calculating the distance, the model will instruct the machine to pick up the object and place it on the required position. If class ‘B’ is present the model will just display the message stating the class is absent.

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How to Cite
Peter, R., Pereira, G., Kamble, Y., & Wagh, M. B. (2023). Automating Machinery with Object Detection using YOLO and Servo Controllers. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 9(1), 23-29.