A Semantic Segmentation Method of In-Vehicle Small Targets Point Cloud Based on Improved RangeNet++Loss Function

Pedestrians and other moving small-targets plays an important role in road scene point cloud.However,they perform poorly for real-time semantic segmentation.Therefore,a small-target semantic segmentation method based on RangeNet++and optimization of loss function is proposed.Firstly,the Focal Loss function is used to improve the original RangeNet++which uses cross entropy as loss function.Then,the weight of some rare,important moving small-targets in Focal Loss function is adjusted.The proposed method can improve the detection and segmentation accuracy of important moving small-targets categories such as pedestrians with fewer training sessions under the DarkNet21 which has fewer convolutional layers.Experiments on the SemanticKITTI dataset show that compared with the original RangeNet++,the improved method uses fewer convolutional layers backbone and fewer training sessions,but achieves higher accuracy and precision in semantic segmentation of moving small-targets.