A dual attention mechanism semantic segmentation method for autonomous driving

Abstract

The paper introduces a refined dual attention mechanism designed to mitigate attention deficiency challenges encountered during the segmentation of diminutive objects within the domain of autonomous driving. In this context, multifaceted factors such as lighting, weather, and road conditions create intricate challenges. The primary objective of this approach is to augment the capacity for feature representation by incorporating position and channel attention mechanisms to derive weights. Initially, the position attention mechanism discerns the significance of each pixel within the spatial domain, generating position-related weights. Subsequently, the channel attention mechanism assesses the importance of each channel in feature representation, resulting in channel-related weights. These derived position and channel attention weights are multiplicatively applied to the input features on an element-wise basis, thereby enhancing their representation capabilities. The resultant features from the two attention modules are amalgamated. Experimental findings substantiate that this enhanced network model significantly elevates the accuracy of semantic segmentation. It achieves an average intersection over union (mIoU) of 80.4% on the Cityscapes dataset, marking a substantial improvement of 10.4% in comparison to the baseline fully convolutional network (FCN) method.

Keywords

• autonomous driving
• semantic segmentation
• attention mechanism
• channel attention
• position attention