1 research outputs found
Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds
Usually, Neural Networks models are trained with a large dataset of images in
homogeneous backgrounds. The issue is that the performance of the network
models trained could be significantly degraded in a complex and heterogeneous
environment. To mitigate the issue, this paper develops a framework that
permits to autonomously generate a training dataset in heterogeneous cluttered
backgrounds. It is clear that the learning effectiveness of the proposed
framework should be improved in complex and heterogeneous environments,
compared with the ones with the typical dataset. In our framework, a
state-of-the-art image segmentation technique called DeepLab is used to extract
objects of interest from a picture and Chroma-key technique is then used to
merge the extracted objects of interest into specific heterogeneous
backgrounds. The performance of the proposed framework is investigated through
empirical tests and compared with that of the model trained with the COCO
dataset. The results show that the proposed framework outperforms the model
compared. This implies that the learning effectiveness of the framework
developed is superior to the models with the typical dataset.Comment: IEEE RED-UAS 2019 Conferenc