Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning
This work was presented at the 10th Joint Symposium on Computational Intelligence (JSCI10), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.
We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.