###### Abstract  
One of the most critical tasks for pavement maintenance and road safety is the rapid and correct identification and classification of asphalt pavement damages. Nowadays, deep learning networks have become the popular method for detecting pavement cracks, and there is always a need to further improve the accuracy and precision of pavement damage recognition. An improved YOLOv4-based pavement damage detection model was proposed in this study to address the above problems. The model improves the saliency of pavement damage by introducing the convolutional block attention module (CBAM) to suppress background noise and explores the influence of the embedding position of the CBAM module in the YOLOv4 model on the detection accuracy. The K-means++ algorithm was used to optimize the anchor box parameters to improve the target detection accuracy and form a high-performance pavement crack detection model called YOLOv4-3. The training and test sets were constructed using the same image data sources, and the results showed the mAP (mean average precision) of the improved YOLOv4-3 network was 2.96% higher than that before the improvement. The experiments indicate that embedding CBAM into the Neck module and the Head module can effectively improve the detection accuracy of the YOLOv4 model. p 202  
using edge detection operators such as the Sobel operator [11], the Prewitt operator [12], and the Canny operator [13; 14]. The threshold segmentation method separates the target cracks from the background by setting a suitable pixel threshold [15; 16]. The region growing method is a method to separate cracks by merging neighboring pixels with similar properties (e.g., grayscale, texture color) into one region [17; 18]. All of these traditional image processing methods have promoted the development of automatic pavement damage detection and identification technology to different degrees [19; 20], but there are also quite a few limitations in practice. Both the edge detection algorithm and the threshold segmentation method are sensitive to image noise [21; 22], so it is difficult to separate the crack morphology completely and accurately. The region growing method, on the other hand, has difficulties in the reasonable selection of initial pixel points. In summary, these types of processing techniques based on single image pixel feature analysis have poor generalization ability and low robustness, and thus are difficult to be applied on a large scale in a real sense. The emergence of deep learning algorithms has given rise to new ideas for pavement image processing in this context. Deep-learning-based target detection techniques can learn the deep features of existing image data and use the similarity between data to predict unknown images. Compared with the traditional image processing methods, this method has higher generalization ability and more accurate detection accuracy. Many studies have been conducted to explore this. [PERSON] et al., proposed region convolutional neural networks (R-CNN) in 2014 [23], a deep learning-based target detection and recognition algorithm that is able to detect the desired target on the basis of the extracted features. Then, in 2015, [PERSON] et al., improved on the R-CNN by proposing the fast R-CNN algorithm [24]. This algorithm enables the classification and localization tasks to not only share convolutional features but also boost each other through region of interest (ROI) pooling and joint multi-task training, and thus the detection accuracy is greatly improved. In the same period, [PERSON] et al., also proposed the faster R-CNN algorithm framework [25]. This algorithm replaced the selective search algorithm with the region proposal network (RPN), which improves the detection speed by sharing convolutional layer features to achieve candidate region extraction and category prediction. These studies have improved the accuracy and speed of target detection to varying degrees but still cannot meet the requirements of real-time image detection.