## 4 The Application of CNN and Other Deep Learning Algorithms
Another challenge in the application of CNN in GPR data is that GPR images are affected by buried soil. The same burial object can show different characteristics in different soils. [PERSON] et al. used multi-task learning (MTL) to solve this problem in 2019. This study combines the two single tasks of object detection and soil type detection. In addition, the soil type detection is used to assist the target detection. The MTL method increases the classification rate effectively [67]. The dependence on manual selection of parameters for GPR image preprocessing is another obstacle to automatic detection. Selecting an appropriate plot scale for the GPR image can enhance the target. Even in the same GPR survey data, the plot scale of different road segments is manually set to different values to generate suitable feature-enhanced GPR images. In this regard, [PERSON] et al. proposed a random incremental sampling (IRS) method referring to the particle filter method. This method generates a batch of random plot scale images for each segment of GPR data and inputs them into the deep learning model. If there are enough random images, the average value can be considered as the true value, so as to realize the automation of GPR system [68]. The R-CNN series algorithm is a classic two-part object detection algorithm. Recently, Faster-RCNN was applied to the detection of targets based on B-scan data in several  
Figure 9:0 The network structure in [62]. studies for its high efficiency. In 2018, [PERSON] et al. transferred the weights of a CNN pretrained on grayscale Cifar-10 into a Faster-RCNN framework and then trained it with real and simulated GPR data. It is proven in this paper that Faster-RCNN outperformed traditional algorithms such as HOG and Haar-like [69] in feature extraction. In 2020, a study aimed at pavement distress detection discussed the performance of cascade CNN, region CNN, and Faster-RCNN, and the result indicated the possibility for a real-time detection with Faster-RCNN [70]. Some other researchers used some auxiliary methods to improve the performance of Faster-RCNN. A work employed various strategies, such as feature cascade, adversarial spatial dropout network (ASDN), Soft-NMS, and data augmentation, to improve the recognition accuracy of Faster-RCNN model for railway subgrade defect [71]. Another study in 2019 also adopted a data augmentation strategy in automatic hyperbola detection based on Faster-RCNN [72]. In 2022, [PERSON] et al. used the Faster R-CNN model to identify and locate underground pipelines and used the Attention-guided Context Feature Pyramid Network (ACFPN) to optimize the feature extraction and used the cascade structure to improve the detection frame regression accuracy. The average recognition accuracy of the proposed model can reach 0.9256. This paper compares the proposed model with Faster R-CNN, YOLO-v5, and Mobilenet-SSD to demonstrate the superiority of the proposed model [73]. In 2022, [PERSON], [PERSON] et al. applied Faster R-CNN to the detection of underground media layering in highways. The model can effectively detect the subsurface interface of the highway, and the accuracy of the model can reach 98.30% [74]. In 2021, [PERSON] et al. used Mask R-CNN to detect and segment semantic features. This paper introduces distance-guided intersection over union (DGIoU) as a new loss function for detecting and segmenting semantic features, overcoming the shortcoming of intersection over union (IoU) in training and evaluation. This paper also proposes a new method for extracting data points, which is robust to the interference of adjacent object features and background noise [75]. The network structure of [75] is shown in Figure 9:1 In the same year, [PERSON] et al. used mask scoring R-CNN (MS R-CNN) as the main framework to monitor and segment target features. They used custom aspect ratio anchor boxes to improve network detection performance and used transfer learning method to improve the problem of insufficient training data.