Efficient Pavement Crack Detection in Drone Images using Deep Neural Networks

Heegwang Kim, Jun Sung, Mingi Kim, Chanyeong Park, J. Paik
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Abstract

Since the drone input images are usually filmed at very high altitudes and with various viewing angles, cracks appear in many different sizes and shapes. A commonly used crack detection dataset has a small size such as 480x320, and it is important that how well it detects the prominent continuous cracks. To detect pavement cracks, we proposed an efficient crack detection network. The proposed network achieved better performance than the conventional network.
基于深度神经网络的无人机图像路面裂缝检测
由于无人机输入的图像通常是在非常高的高度和不同的视角拍摄的,裂缝出现在许多不同的大小和形状。通常使用的裂纹检测数据集具有较小的尺寸,例如480x320,并且重要的是它如何检测突出的连续裂纹。为了检测路面裂缝,我们提出了一种高效的裂缝检测网络。该网络取得了比传统网络更好的性能。
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