# Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection Source: [Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:49:06.844Z Authors: Jiahe Fan, Mohammud J. Bocus, Brett Hosking, Rigen Wu, Yanan Liu, Sergey Vityazev, Rui Fan Published: 2021-12-24T15:07:47.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()