Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection
This work addresses pothole detection for road maintenance, presenting an incremental improvement over existing single-modal semantic segmentation networks.
The paper tackles road pothole detection by proposing a novel single-modal semantic segmentation method that uses channel attention, atrous spatial pyramid pooling, and multi-scale feature fusion, achieving state-of-the-art performance on the Pothole-600 dataset for both RGB and disparity images.
This paper presents a novel pothole detection approach based on single-modal semantic segmentation. It first extracts visual features from input images using a convolutional neural network. A channel attention module then reweighs the channel features to enhance the consistency of different feature maps. Subsequently, we employ an atrous spatial pyramid pooling module (comprising of atrous convolutions in series, with progressive rates of dilation) to integrate the spatial context information. This helps better distinguish between potholes and undamaged road areas. Finally, the feature maps in the adjacent layers are fused using our proposed multi-scale feature fusion module. This further reduces the semantic gap between different feature channel layers. Extensive experiments were carried out on the Pothole-600 dataset to demonstrate the effectiveness of our proposed method. The quantitative comparisons suggest that our method achieves the state-of-the-art (SoTA) performance on both RGB images and transformed disparity images, outperforming three SoTA single-modal semantic segmentation networks.