Diffusion-based road defect detection model integrating edge information and efficient multi-scale convolution

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-05-22 DOI:10.1016/j.asoc.2025.113332

Xueqiu Wang , Huanbing Gao , Zemeng Jia

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引用次数: 0

Abstract

Roads are vital infrastructure components, and the prompt detection and repair of defects are critical for their longevity and safety. This paper introduces the Edge Efficient Multi-Scale Focusing Diffusion Network (EEFNet), a precise method for road defect detection. The Edge Information Enhancement Module (EIEM) accentuates crack contours while minimizing background noise. An Efficient Multi-Scale Convolution (EMSConv) is proposed. The EMSConv captures features across multiple scales, thereby enhancing model efficiency through reduced computational demands and parameter count. The Focusing Diffusion Pyramid Network (FDPN) collects and distributes context-rich features across various scales using a diffusion mechanism, thereby improving detection capabilities. Additionally, the Task Dynamic Align Detection Head (TADDH) facilitates parameter sharing among detection heads, which enhances classification and localization accuracy. EEFNet has demonstrated a 92.7 % accuracy rate at 126 FPS (Frames Per Second) on a road defect dataset and has proven robustness on several diverse datasets including Unmanned Aerial Vehicle Asphalt Pavement Distress Dataset (UAPD), Visual Object Classes 2007 (VOC2007), Global Road Damage Detection2022 (GRDD2022), and Vision Meets Drone 2019 (Visdrone2019). In addition, by pruning the model and deploying it onto edge computing devices, practical experiments have demonstrated that the EEFNet model has substantial practical application value.

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基于边缘信息和高效多尺度卷积的道路缺陷扩散检测模型

道路是重要的基础设施组成部分，及时发现和修复缺陷对道路的寿命和安全至关重要。本文介绍了边缘高效多尺度聚焦扩散网络（Edge - Efficient Multi-Scale focused Diffusion Network, EEFNet）——一种用于道路缺陷检测的精确方法。边缘信息增强模块（EIEM）突出裂纹轮廓，同时最大限度地减少背景噪声。提出了一种高效的多尺度卷积（EMSConv）算法。EMSConv捕获多个尺度的特征，从而通过减少计算需求和参数计数来提高模型效率。聚焦扩散金字塔网络（FDPN）利用扩散机制在不同尺度上收集和分布上下文丰富的特征，从而提高检测能力。此外，任务动态对齐检测头（TADDH）便于检测头之间的参数共享，提高了分类和定位精度。EEFNet在道路缺陷数据集上以每秒126帧的速度展示了92.7 %的准确率，并在多个不同的数据集上证明了鲁棒性，包括无人机沥青路面破损数据集（UAPD）、视觉对象类别2007 （VOC2007）、全球道路损伤检测2022 （GRDD2022）和视觉与无人机2019 （Visdrone2019）。此外，通过对模型进行修剪并将其部署到边缘计算设备上，实际实验表明，EEFNet模型具有较大的实际应用价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.