Accuracy Improvement of Semantic Segmentation Trained with Data Generated from a 3D Model by Histogram Matching Using Suitable References

Visual navigation based on the results of semantic segmentation requires high classification accuracy. Previous research has proven that a classifier of semantic segmentation trained upon a dataset generated from a 3D model performs well when the input images are also generated from a 3D model. However, when the input images are real 2D images captured at the same location by a camera mounted on a robot, the average classification accuracy deteriorates. To overcome this issue, a novel scheme is proposed to improve the classification accuracy of semantic segmentation when the training data is generated from a 3D point cloud. The key features of the proposed scheme are filling in the missing data by inpainting and domain adaptation by histogram matching. To evaluate the proposed scheme, datasets composed of real images captured during a variety of seasons, weathers, and times were created. Experimental results showed that ICNet trained upon our dataset could provide accurate results for visual navigation.