# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
## 2 Proposed Approaches  
### Acquisition and Pre-Processing GPR Images  
GeoScope 3D Radar was used to collect 3D GPR data, which mainly consisted of a GeoScopeTM MK IV mainframe, a DXG1212 shallow ground-coupled antenna array, an RT3D acquisition software, and a 3 dr-Examiner data processing software, as shown in Figure 1. The antenna matching mode was a conventional mode with 12 channels; trigger spacing was 5 cm; time window was 25 ns; dwell time was 3 \(\upmu\)s. A 3D GPR using a stepping frequency of 100-3000 MHz and antenna array technology captured the full range of wavelengths of pavement interior information during data acquisition, achieving a balance between depth and resolution in a single acquisition. The investigated road sections were on Zhangshu-Ji'an Highway, Jiangxi, China. Structure types I-IV in Figure 2 show four main types of pavement structures on the highway. The lengths of the road sections with structure types I, II, III, and IV are 21.9 km, 8.3 km, 175.4 km, and 4.0 km, respectively. The four structures were the most common in China. All 1574 original GPR images were collected and each of them represents a road section with a length of 60 m and a width of 0.8 m. The 416 \(\times\) 416 resolution image is a common input size for the YOLO model, and the detector can detect anomalous waveforms well at this resolution. Therefore, these images were cropped and resized to finally obtain 3688 horizontal radar images with a resolution of 416 \(\times\) 416, as shown in Figure 3. The actual width and length of each cropped radar image were 0.8 m and 20 m, respectively. Figure 1: GeoScope 3D Radar equipment: (**a**) radar mainframe, (**b**) multichannel antenna array, (**c**) Layout of the DXG1212 antenna array, where the numbers 1–7 indicate the serial number. Figure 2: Zhangshu-Ji’an Highway, which had four different pavement structures. A crack is a typical type of distress in the pavement and appears in horizontal radar images as stripes with a strong reflective appearance. Pavement repairs were performed to prevent the pavement from deteriorating. The waveform at the repair location would show a highlight feature compared to its nearby location due to the difference in repair materials and raw materials. Voids refers to the phenomenon of bottom cavity of the pavement structure layer caused by settlement and distortion between the old and new asphalt pavement. It is often depicted as a blocky highlight on horizontal radar images. Due to environmental constraints during construction, there was poor interlayer bonding at the interlayer. It appears as a highlight anomaly on horizontal radar images. Mixture segregation is the result of an uneven paving process due to poorly mixed materials or uncontrolled temperatures during production, mixing and paving. This mixture segregation is often depicted as a messy highlight feature on horizontal radar images due to the large number of voids. Typical pavement structural distresses (cracks, repairs, voids, poor interlayer bonding, and mixture segregation) were shown in Figure 4. Different distresses were represented clearly in the horizontal radar images. Therefore, horizontal radar images provide a unique perspective for pavement distress detection and can be used as an aid for pavement inspection. However, the horizontal radar images still had two problems. First, the waveform characteristics were complex because the same pavement distress may have shown different characteristics at different depths. For example, voids may be shown as black or white highlights at different depth locations. In addition, due to the complex and various internal structures of pavements, except for typical pavement distresses, there were also a large number of noise waveforms that derive from many real-world factors, such as antenna vibration and background noise. These anomalous waveforms significantly affect distress detection performances. Therefore, a robust and accurate method was needed to process the horizontal radar images.