1. Introduction
Aging road infrastructure necessitates efficient and automated methods for damage detection, which is crucial for timely maintenance and public safety. Traditional methods often struggle with the varied appearances, scales, and elongated shapes of road damages like cracks and potholes. This paper addresses these challenges by proposing StripRFNet, a novel network architecture. The article primarily uses the StripRFNet model, and compares it against models such as U-Net, DeepLabv3+, and RetinaNet.
2. Related Work
Prior research in road damage detection has explored various deep learning models, including convolutional neural networks and transformer-based architectures. Many existing semantic segmentation models like U-Net and DeepLabv3+ have been adapted, but often lack specific mechanisms to effectively handle the anisotropic and irregular shapes of road damages. Object detection approaches such as YOLO and RetinaNet also face challenges in precise localization and segmentation of fine cracks. StripRFNet aims to overcome these limitations by introducing specialized receptive field structures.
3. Methodology
StripRFNet integrates specialized strip receptive fields that can capture features along elongated directions, crucial for detecting cracks and similar damages. The network's architecture is further enhanced with a shape-aware module designed to adaptively focus on the geometric properties of different damage types. This involves a multi-branch design where each branch extracts features with different orientations, followed by an aggregation module. The training process employs a combination of standard cross-entropy loss and a shape-sensitive loss function.
4. Experimental Results
Experiments were conducted on several publicly available road damage datasets, demonstrating StripRFNet's superior performance across key metrics. The model achieved significantly higher F1-score and Mean Intersection over Union (mIoU) compared to state-of-the-art baselines. Specifically, StripRFNet showed robust generalization capabilities and improved detection accuracy for fine cracks and small potholes. The table below summarizes the comparative performance.
| Model | F1-score (%) | mIoU (%) |
|---|---|---|
| U-Net | 78.5 | 65.2 |
| DeepLabv3+ | 81.2 | 68.9 |
| RetinaNet | 76.1 | 62.5 |
| StripRFNet (Proposed) | 85.8 | 74.3 |
5. Discussion
The superior performance of StripRFNet validates the efficacy of incorporating strip receptive fields and shape-aware mechanisms for road damage detection. This approach significantly enhances the model's ability to discern complex and anisotropic damage patterns, which are often challenging for generic segmentation models. The robust results suggest promising applications in real-time road inspection and automated maintenance scheduling. Future work will focus on deploying StripRFNet on edge devices and exploring its adaptability to diverse environmental conditions.