Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques
Shuangxi Zhou, Dan Yang, Ziyu Zhang, Jinwen Zhang, Fulin Qu, Piyush Punetha, Wengui Li, Ning Li
Abstract
• Introduces advanced optimizations to the YOLOv5s algorithm for faster and more accurate pavement crack detection. • Demonstrates significant improvements in detection speed and accuracy through rigorous field testing. • Explores novel attention mechanisms that enhance the model’s ability to identify and classify diverse crack types. • Outlines potential future advancements for reducing computational requirements and enabling real-time detection on mobile devices. To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the YOLOv5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from YOLOv6 diminishes memory usage and expedites detection speed. By replacing the original nearest up-sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined YOLOv5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure.