Overview and optimization strategy of road crack detection based on YOLOv8 algorithm

This paper comprehensively reviews the YOLOv8 algorithm and its application in road crack detection, focusing on the advantages of the algorithm in real-time target detection and high accuracy. With the continuous expansion of road infrastructure around the world, road cracks have become a serious safety hazard, which not only poses a threat to drivers but also brings a huge economic burden. Traditional road crack detection methods, such as manual inspection and sensor technology, often face the problems of high labor intensity, low efficiency and high cost. With the development of computer vision and deep learning technology, YOLOv8, as an efficient target detection system, provides new possibilities for the automation and accuracy improvement of road crack detection. This paper reviews different application versions of YOLOv8 in road crack detection, evaluates their performance, and points out the challenges of small target detection, real-time processing and multi-scale feature fusion. This paper proposes a variety of optimization strategies, including model optimization, data enhancement, training strategy and hardware acceleration. In addition, this paper looks forward to the future development trends of deep learning in the field of road crack detection, such as multimodal fusion, cross-domain detection and automated system integration, to promote the improvement of road safety and maintenance efficiency.