A GAN-Based Domain Adapted Deep Learning Pipeline for Object Detection in an Intralogistics Warehouse Environment

Abstract

Perception is crucial for Autonomous Mobile Robots (AMRs) operating in warehouses to recognize load carriers using vision-based guiding systems. Convolutional Neural Networks (CNN s), while frequently utilized in object detection networks, exhibit significant performance degradation when evaluated in new environments. Although creating computer simulations is a straight-forward option, their usefulness in real-world applications is restricted by stylistic disparities between simulated and real data. Recently, Generative Adversarial Networks (GANs) have demonstrated improved performance in Unpaired Image-to-Image Translation (UNIT) tasks, indicating that they might be a viable choice for reducing the stylistic difference between simulated and actual data. This work proposes a domain adaptation-based deep learning pipeline for object detection in a warehouse setting. The detection pipeline is trained with synthetic data and then tested on a real warehouse dataset. A UNIT GAN model is used to bridge the gap between the synthetic and real worlds. The GAN model was evaluated for its overall efficacy in recognizing pallets in a real-world warehouse testing dataset. The findings indicate that the GAN model is effective in bridging the synthetic-real domain gap. Finally, the study gives fresh insight on the potential performance enhancements that may be achieved by training the detection network using both adapted and non-adapted synthetic data.