Our recent research has been published in a journal!
I’m delighted to share our study, “Contamination Detection Using a Deep Convolutional Neural Network with Safe Machine Environment Interaction,” has been accepted for publication in MDPI Electronics Journal. This journey has been an effort of dedication, and I can’t wait to share our groundbreaking findings with you.
Understanding the Problem
Contamination detection is an important issue in many industries, including food processing, healthcare, and others. A primary focus is ensuring product quality and safety, and this is where our research comes in. Manual examination gets laborious and may result in contamination occurring along the production line. To solve this issue, a contamination detection system based on an enhanced deep convolutional neural network (CNN) in a human-robot collaboration framework is proposed.
Deep Learning: The Key to Precision
In our research, we enhanced Deep Convolutional Neural Networks (CNNs) to detect contaminants in food packages. CNNs are well-known for their ability to extract intricate patterns from complex data, making them perfect for tasks like object detection and analysis.
Safe Machine Environment Interaction
To improve our system’s performance, we coded the proximity sensor for “Safe Machine Environment Interaction.” A mechatronic platform with a camera for contamination detection and a time-of-flight sensor for safe machine-environment interaction was used for the experiment. The experiment findings show that the reported system can identify contamination with 99.74% mean average precision (mAP). Figure 1 depicts the experimental results, and the publication can be found here .
Future work may concentrate on adding more contamination classes in order to create a more thorough and improved contamination detection system. The algorithm might also be applied in a real robot with a conveyor belt to create an industrial quality inspection setup. However, the journey does not finish here. We’re devoted to improving our methodology and investigating new applications. We’re thrilled to be at the forefront of the deep learning revolution.
I want to express my heartfelt gratitude to my Supervisor, colleagues, and the entire research team who played a pivotal role in this project. Their expertise, dedication, and collaboration made this achievement possible.
 Hassan, Syed Ali, Muhammad Adnan Khalil, Fabrizia Auletta, Mariangela Filosa, Domenico Camboni, Arianna Menciassi, and Calogero Maria Oddo. “Contamination Detection Using a Deep Convolutional Neural Network with Safe Machine—Environment Interaction.” Electronics 12, no. 20 (2023): 4260.