We compared the performance of three well-known deep learning approaches for object detection on the AGAR dataset, namely two-stage, one-stage, and transformer-based neural networks.
The two-stage detector for litter localization and classification is presented, namely, EfficientDet-D2 is used to localize litter, and EfficientNet-B2 to classify the detected waste into seven categories.
We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion.
We introduced the Annotated Germs for Automated Recognition (AGAR) dataset, an image database of microbial colonies cultured on agar plates. It contains 18000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras.
The detect-waste team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem.
Using detection models to localize and classify waste on images and video.
The aim of the project is to develop a method to automate the analysis of bacterial colonies on Petri dishes using artificial neural networks and Machine Learning algorithms.