## 2 Related Work on Pothole Detection  
Although the use of deep CNNs for pothole detection has been studied for several years [7; 11; 25; 27; 29; 30], efficient algorithms for this task are still limited. In many scenarios, the pothole object is relatively small compared to the size of the road image, making it challenging to train a CNN on a high-resolution image due to the large amount of memory required. In a recent study by [PERSON] et al. [9], the performance of object identification and semantic segmentation algorithms was evaluated based on detection time and overall system accuracy. The results indicated that while segmentation algorithms had high accuracy, they often came with an increase in computational complexity. YOLO version 3 (v3) was found to be faster and achieve a better mean Average Precision (mAP) than YOLOv2. [PERSON] et al. [30] presented a method for automated pothole detection based on YOLO models. Three YOLO models (YOLOv4, YOLOv4-tiny, and YOLOv5) were used in the training and testing phases, with a dataset of 665 pothole images divided into training, validation, and testing subsets. The results showed that the [EMAIL_ADDRESS] values of YOLOv4, YOLOv4-tiny, and YOLOv5 were 77.7%, 78.7%, and 74.8%, respectively, with YOLOv4-tiny having the best performance in detecting potholes. This study has limitations, such as low accuracy in detecting small, distant potholes and a lack of testing under poor weather and lighting conditions. [PERSON] et al. [27] presented a pothole detection method based on CNN pre-pooling. The dataset comprised 400 raw pothole images collected from different pavements under varying lighting conditions, cropped into 96,000 small images for training or testing. The proposed method utilized a pre-pooling layer in the CNN architecture, improving accuracy compared to conventional CNN. The authors analysed the proposed method's advantages in terms of accuracy, robustness to different lighting and pavement conditions, and superiority over conventional methods (e.g., Sobel edge detection and K-means cluster analysis). [PERSON] [11] performed an extensive evaluation of various object detection architectures, including YOLOv5 models (for three different model sizes) with ResNet101 backbone, YOLOR, Faster R-CNN with ResNet50, VGG16, MobileNetV2, and InceptionV3 backbones. The author also proposed a modified VGG16 (MVGG16) architecture, which effectively reduced computational costs while retaining detection accuracy. Upon comparison, Faster R-CNN with ResNet50 achieved the highest precision rate of 91.9% and an inference time of 0.098 s for larger images. YOLOv5, on the other hand, offered the fastest inference speed with an inference time of 0.009 s but at the cost of reduced accuracy, making it more suitable for real-time applications. [PERSON] et al. [29] proposed a pothole detection method based on localization and partial-classification CNNs. The proposed method comprised two subnetworks: Localization Network (LCNN) and the Part-based Classification Network (PCNN). The LCNN utilized a high recovery network to identify candidate regions, and the PCNN performedclassification on the candidates. The authors used two different image sizes as inputs to the network to balance accuracy and efficiency. The results showed that the accuracy, precision, recall, and F1-score were 95.0%, 95.2%, 92.0%, and 93.6%, respectively, with the proposed method outperforming existing methods. The proposed method has the potential for scalability to detect not only potholes but also other road defects, but its performance may degrade with low-resolution input data. The authors suggest using scale selection and cascading techniques to further improve performance. [PERSON] et al. [7] proposed improvements in YOLOv4 Tiny through the integration of two multi-scale feature modules, such as the Spatial Pyramid Pooling (SPP) and Feature Pyramid Network (FPN) with CSPDarknet53-tiny backbone.