Improved Mask R-CNN Multi-Target Detection and Segmentation for Autonomous Driving in Complex Scenes

Vision-based target detection and segmentation has been an important research content for environment perception in autonomous driving, but the mainstream target detection and segmentation algorithms have the problems of low detection accuracy and poor mask segmentation quality for multi-target detection and segmentation in complex traffic scenes. To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. Furthermore, a bottom-up path enhancement strategy was added to the Feature Pyramid Network (FPN) to achieve feature fusion, while an efficient channel attention module (ECA) was added to the backbone feature extraction network to optimize the high-level low resolution semantic information graph. Finally, the bounding box regression loss function smooth L1 loss was replaced by CIoU loss to speed up the model convergence and minimize the error. The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. The migration experiments showed that it has good detection and segmentation effects in each traffic scenario of the publicly available BDD autonomous driving dataset.

Automated summary

Vision-based target detection and segmentation is crucial in autonomous driving. However, the mainstream algorithms suffer from low detection accuracy and poor segmentation quality in complex traffic scenes. To tackle this issue, this paper enhances the Mask R-CNN model by using the ResNeXt network as the backbone, implementing a bottom-up path enhancement strategy for feature fusion, incorporating an efficient channel attention module to optimize high-level semantic information, and replacing the bounding box regression loss function with CIoU loss for faster convergence and reduced errors. Experimental results demonstrate that the improved Mask R-CNN achieves better performance than the original algorithm in terms of target detection and segmentation accuracy.