VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View

DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block

“…Through the gradual experiments of the three methods in B, C, D of this section, we achieve 7.7%, 2.6%, 1.8% improvements respectively on major metric mIoU, and get a total of 12% promotion. We compare our model with other models in [16] on the PSV dataset. The detailed results are shown in Table IV, ours get the advanced result on mIoU, and majority best on the IoU of each class.…”

“…This is because the accuracy of deep learning model is largely related to datasets, and there are few public dataset of panoramic images which can be used to train a model. The first public panoramic dataset for lane markings and parking slots is panoramic surround view (PSV) dataset, released by Yan Wu et al [16]. And this dataset is specially used for semantic segmentation, which is labeled pixel by pixel, and each pixel has its corresponding class.…”

“…In [16], they achieved the segmentation of lane markings and parking slots using semantic segmentation method on PSV dataset, and proposed a VH-stage module special for linear structures. But the size of their model is too large to meet the requirement of using in embedded and mobile platform.…”

“…In our experiments, we evaluate our methods on PSV datasets [16], which is made and released by The Tongji Intelligent Electric Vehicle (TiEV) team. The images are collected in Tongji university with two size, 600x600 and 1000x1000.…”