• metal
• scrap
• object detection
• instance segmentation
• semantic segmentation
• image classification
• deep learning
• convolutional neural networks

This work aims at investigating the use of Deep Learning techniques to automatically detect and classify, through CCTV images, the scrap material entering the industrial site using a set of images collected by Danieli Automation.
The primary objective is evaluating the performances of Deep Learning models and libraries in scrap object segmentation and non-conforming object detection tasks. The secondary objective is to ensure the possibility of integrating such models in a web-based online tool that can be queried by technical personnel.
The object segmentation task was approached both as an Instance segmentation and a Semantic segmentation problem.
The instance segmentation problem was tackled by implementing a pipeline that includes a DetectoRS model with a mask branch plus a post-processing stage. The semantic segmentation problem comprises the generation of suitable semantic annotations from the original dataset and a UPERnet model with a Vision Transformer backbone that outperforms the instance, segmentation model.
The non-conforming object detection task was conducted in two successive phases. The first comprises a careful annotation of the non-conforming dataset and subsequently, a DetectoRS model was implemented whose performance are much higher than a baseline obtained with a Faster R-CNN
All the developed models are integrated into an inference pipeline and served by a dash based interface that can be queried by uploading a CCTV image and performing the required task