Research And Implementation Of Semantic Segmentation Algorithm For Autonomous Driving Scene

As a pixel-level classification task in the field of computer vision,the goal of semantic segmentation is to accurately classify each pixel in the image,and applying semantic segmentation algorithms to autonomous driving scenarios requires higher accuracy and faster reasoning speed.On the one hand,most of the current mainstream semantic segmentation algorithms use large-scale convolutional neural networks for the pursuit of accuracy,thus generating a huge number of parameters and requiring huge computing power support,resulting in slow model reasoning and difficult deployment in autonomous driving scenarios.On the other hand,there are also many cutting-edge works that deliberately pursue real-time reasoning speed and overuse lightweight semantic segmentation networks,resulting in insufficient feature extraction capabilities of the model and poor overall classification accuracy,which cannot be directly applied to autonomous driving scenarios.Therefore,in the research of semantic segmentation algorithms for autonomous driving scenarios,how to balance accuracy and real-time performance is a difficult challenge and a hot issue in the industry.In response to the above problems,this paper conducts in-depth research on semantic segmentation methods in autonomous driving scenarios.The main work is as follows:(1)In view of the semantic confusion of similar objects in autonomous driving scenes,this paper uses the improved MobileNet v2 as the encoder,proposes a semantic segmentation model based on context cascade,and designs an effective and lightweight context cascade module,which combines three dense cascaded dilated convolution modules with different dilated ratios,and performs channel fusion through short-term dense cascading,aiming to capture rich multi-scale context information.Experiments show that the context cascading module only increases the computational cost by a small amount,improves the overall accuracy of the model,and effectively improves the segmentation accuracy of similar objects.(2)In view of the poor performance of small target segmentation in autonomous driving scenes,this paper proposes a semantic segmentation model based on multi-scale feature refinement,and designs an efficient multi-scale feature refinement module.The low-dimensional and multi-scale spatial features are obtained by the parallel structure of high efficiency,and then the channel attention mechanism is used to realize the highdimensional features to constrain the captured low-dimensional and multi-scale spatial features,thereby suppressing the noise information in the low-dimensional features,and finally combined with the high-dimensional features.In order to promote the effective feature fusion of high-dimensional features and low-dimensional features,it can effectively refine the spatial detail information with a relatively small amount of calculation cost,thereby improving the segmentation accuracy of small objects.The effectiveness of the proposed method is verified by comprehensive evaluation on the Cityscapes dataset and the Camvid dataset.(3)This paper integrates the above algorithms and designs a semantic segmentation system for autonomous driving scenarios,which realizes the import of local urban landscape images,calls the model to perform semantic segmentation processing on the input images,and visualizes the segmentation effect.