ABSTRACT

Regular inspection of pavement cracks is an important task to ensure the safety of the transportation system. At present, many pavement crack detection methods still rely on the manual way. These methods are usually time-consuming and subjective. Moreover, although the automatic crack detection method has made great progress recently, there are still difficulties such as poor anti-interference ability and low detection efficiency. Therefore, this paper proposes a pavement crack detection algorithm, which can solve the above problems well. This algorithm combines single stage salient-instance segmentation (S4Net) and concatenated feature pyramid network (CFPN), which greatly improves the ability to acquire feature information. Experiments show that on the noise-free dataset, the average precision, average recall, and F1-score are 0.9331, 0.9358, and 0.9344, respectively. On the complex noise dataset, the average precision, average recall, and F1-score are 0.8244, 0.8653, and 0.8443, respectively. Compared with other methods, our method has the advantages of strong anti-noise ability, high detection accuracy and fast detection speed. In addition, we propose a method for calculating the physical size of cracks. Through error analysis, the relative errors of calculating the length and width of the cracks are 0.056 and 0.084 respectively, which can meet the needs of engineering inspection.

摘要

定期检查路面裂缝是确保交通系统安全的一项重要工作。目前，许多路面裂缝检测方法仍然依赖于人工方式。这些方法通常既费时又主观。此外，虽然近来裂纹自动检测方法取得了很大进展，但仍存在抗干扰能力差、检测效率低等难点。因此，本文提出了一种路面裂缝检测算法，可以很好地解决上述问题。该算法结合了单阶段显着性实例分割（S4Net）和级联特征金字塔网络（CFPN），大大提高了获取特征信息的能力。实验表明，在无噪声数据集上，平均精度、平均召回率和 F1-score 分别为 0.9331、0.9358、和 0.9344，分别。在复杂噪声数据集上，平均精度、平均召回率和 F1 分数分别为 0.8244、0.8653 和 0.8443。与其他方法相比，我们的方法具有抗噪声能力强、检测精度高、检测速度快等优点。此外，我们提出了一种计算裂缝物理尺寸的方法。通过误差分析，计算得到的裂缝长度和宽度的相对误差分别为0.056和0.084，能够满足工程检测的需要。我们提出了一种计算裂缝物理尺寸的方法。通过误差分析，计算得到的裂缝长度和宽度的相对误差分别为0.056和0.084，能够满足工程检测的需要。我们提出了一种计算裂缝物理尺寸的方法。通过误差分析，计算得到的裂缝长度和宽度的相对误差分别为0.056和0.084，能够满足工程检测的需要。