Department of Civil Engineering, Kyungpook National University, Daegu 37224, Republic of Korea.
Sensors (Basel). 2022 Dec 7;22(24):9599. doi: 10.3390/s22249599.
An efficient road damage detection system can reduce the risk of road defects to motorists and road maintenance costs to traffic management authorities, for which a lightweight end-to-end road damage detection network is proposed in this paper, aiming at fast and automatic accurate identification and classification of multiple types of road damage. The proposed technique consists of a backbone network based on a combination of lightweight feature detection modules constituted with a multi-scale feature fusion network, which is more beneficial for target identification and classification at different distances and angles than other studies. An embedded lightweight attention module was also developed that can enhance feature information by assigning weights to multi-scale convolutional kernels to improve detection accuracy with fewer parameters. The proposed model generally has higher performance and fewer parameters than other representative models. According to our practice tests, it can identify many types of road damage based on the images captured by vehicle cameras and meet the real-time detection required when piggybacking on mobile systems.
一个高效的道路损坏检测系统可以降低驾驶者遇到道路缺陷的风险和道路交通管理部门的道路维护成本，为此，本文提出了一种轻量级的端到端道路损坏检测网络，旨在快速、自动地准确识别和分类多种类型的道路损坏。所提出的技术包括一个基于轻量级特征检测模块的骨干网络，该模块由一个多尺度特征融合网络组成，与其他研究相比，这更有利于在不同距离和角度进行目标识别和分类。还开发了一个嵌入式轻量级注意力模块，通过为多尺度卷积核分配权重来增强特征信息，从而在使用更少参数的情况下提高检测精度。与其他有代表性的模型相比，所提出的模型通常具有更高的性能和更少的参数。根据我们的实践测试，它可以根据车载摄像头拍摄的图像识别多种类型的道路损坏，并满足在移动系统上进行实时检测的要求。
