Landscape design of aging rehabilitation in community park based on improved YOLOv4

Abstract

In the aging rehabilitation landscape design of community parks, the crack detection of landscape architecture is very important, which is related to the beauty and safety of landscape architecture. In order to improve the poor effect of current landscape building crack detection methods, an intelligent landscape building crack detection model based on improved YOLOv4 is proposed. Aiming at the inadequacy of information feature extraction in YOLOv4 algorithm, the coordinate attention module with extended convolution structure optimization is adopted to optimize it. Aiming at the defect that YOLOv4 algorithm has poor information feature utilization rate of shallow position, an improved K-means algorithm is designed to determine the optimal size of prior frame, so as to improve the performance of the algorithm. The performance of the algorithm is improved. Finally, combined with the above content, an intelligent detection model of landscape building cracks based on improved YOLOv4 is constructed. The results verify that the fitting degree of the model is 0.986, which is 0.008 and 0.016 higher than that of the CRF-CNN and Faster-RCNN models, respectively. The F1 value is 0.951, 0.10–0.12 higher than the two models. The accuracy was 98.26%, 0.47%-0.51% higher than the two models. Recall value was 96.44%, which was 0.47%-0.51% higher than that of the two models. The AUC value is 0.988, which is 0.009–0.016 higher than the two models. To sum up, the model built in the study can detect the cracks in landscape architecture with high accuracy and efficiency, and improve the safety and beauty of the landscape. Meanwhile, this model can assist in the design of aging rehabilitation landscape in community parks.

Keywords

• YOLOv4
• Crack detection
• K-means
• Attention module
• Landscape design