## 1 Introduction
### Pothole Detection  
The location of road potholes may be determined in a number of different ways. Low-cost methods based on accelerometers measuring vibration can have a high degree of accuracy. Although their performance is independent on visibility conditions, their reaction time may be slow, and the vehicle must drive over a pothole to make detection [14; 15; 16]. Three-dimensional reconstruction techniques using laser [17; 18] or stereo vision [19; 20; 21] can be utilized to model point cloud representations of road inconsistencies. 3D lasers based on the principle of transmitting light pulses to the target object/surface can detect objects in low visibility but come at a higher cost. Stereo vision systems can be susceptible to vibrations and need several camera alignments as well as significant surface reconstruction calculations. Figure 1: Detection outcomes under clear weather conditions. Deep-learning-based object detection has been utilized in research to perform accurate and quick pothole detection. Object detection is a technique used to identify and classify objects within an image while determining their location. It is a critical challenge in the field of computer vision, and it has been the subject of intensive research and development over the course of the last several years. With the rapid development of deep learning, significant advancements have been made in improving object recognition performance using deep neural models. Among these models, Convolutional Neural Networks (CNNs) are considered one of the most innovative breakthroughs in image processing. One of the key advantages of using CNNs for object detection is that they can be trained end to end on large datasets, allowing us to learn complex features and representations of objects. Region-based CNNs (R-CNNs) were among the first deep-learning-based object detection methods, proposing a CNN to extract features from input images and Support Vector Machines (SVMs) to classify objects [22]. However, their slow processing speed limits their practical use. Fast R-CNN [23] was later introduced to improve R-CNN by adding a Region-of-Interest (RoI) pooling layer that reduced processing time and allowed for end-to-end training of the network. You Only Look Once (YOLO) [24], a single-stage architecture, significantly improved detection accuracy, making it comparable to and, in some cases, even superior to R-CNN models. Providing significant accuracy and speed of inference, the YOLO models and their modifications appeared most often in research works regarding pothole detection. In addition, Faster R-CNN was also deployed for improved visual representation and detection accuracy. Although inference speed is a limitation of Faster R-CNN compared to newer versions of YOLO, there has been significant development of models in the R-CNN family recently. In addition to the aforementioned architectures, EfficientDet [25], Single-Shot Detector (SSD) [26], and RetinaNet [10] are other models used for pothole detection. EfficientDet uses a compound scaling method to optimize the detection of objects at various sizes and scales. It achieves high accuracy with fewer parameters, making it a more efficient architecture for real-time applications. Similarly, RetinaNet uses a novel loss function to address the issue of class imbalance and improve the detection of smaller objects. SSD, on the other hand, simplifies the object detection pipeline by eliminating the region proposal step, resulting in faster detection times. A detailed overview of works related to pothole detection is given in Section 2. Despite the progress that has been made in pothole detection using CNNs, there are still several challenges that need to be addressed to make this technology more practical and effective [27; 28; 29]. These include the need for larger and more diversified training datasets and algorithms capable of detecting potholes in real time and operating in a range of real-world settings, such as changing lighting and weather conditions.