Multi-task learning method for real-time target detection and semantic segmentation based on lightweight network

Abstract

The invention relates to a multi-task learning method for real-time target detection and semantic segmentation based on a lightweight network. The system comprises a feature extraction module, a semantic segmentation module, a target detection module and a multi-scale receptive field module. The feature extraction module selects a lightweight convolutional neural network MobileNet; features are extracted through a MobileNet network and sent to a semantic segmentation module to complete segmentation of a drivable area and a selectable driving area of a road, and meanwhile the features are sentto a target detection module to complete object detection appearing in a road scene. A multi-scale receptive field module is used for increasing the receptive domain of a feature map, convolution of different scales is used for solving the multi-scale problem, finally, weighted summation is carried out on a loss function of a semantic segmentation module and a loss function of a target detection module, and a total module is optimized. Compared with the prior art, the method provided by the invention has the advantage that two common unmanned driving perception tasks of road object detection and road driving area segmentation are completed more quickly and accurately.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and more specifically relates to a multi-task learning method of real-time target detection and semantic segmentation based on a lightweight network. Background technique [0002] Computer vision is gaining popularity in autonomous driving, mainly due to the rise of neural network-based deep learning techniques. The emergence of more and more public datasets and well-developed hardware resources has promoted related research results and further promoted the development of computer vision technology. Many computer vision tasks are used in self-driving cars, such as object detection and road segmentation, which are critical for perceiving the driving environment. The current trend is to continuously improve the accuracy of these tasks while keeping the inference time as short as possible. Only satisfying the accuracy of model perception without a fast model prediction speed will bring gre...

AI Technical Summary

Problems solved by technology

Moreover, there is a problem that the scales of the objects in the road scene are too large, and the conventional model cannot accurately complete the perception of large objects and small objects at the same time, so many potential problems will erupt.

Images