A Novel Approach for Concrete Crack and Spall Detection Based on Improved YOLOv8

The detection and prevention of concrete cracks and spalls is crucial to ensure the structural integrity and longevity of civil infrastructure. In this research paper, we propose a novel method for concrete crack and spall detection based on YOLOv8 with ByteTrack and supervision. The proposed method exploits the advantages of the YOLOv8 object detection framework, which provides real-time and accurate detection, tracking and counting of various objects. By integrating ByteTrack, a state-of-the-art tracking algorithm, we increase system performance and robustness in tracking cracks and spalls over time. Furthermore, supervision is employed to improve detection accuracy through iterative training and fine-tuning. To train the model, an extensive dataset of concrete crack and spall images is collected, annotated, and enhanced. The dataset includes a variety of scenarios, lighting conditions, and crack/spill sizes, which ensures the model's ability to generalize to real-world situations. Transfer learning is used to support the YOLOv8 backbone with pre-trained weights, to accelerate the convergence of the training process. Experimental evaluation is conducted on a benchmark dataset, and the proposed method outperforms existing techniques in terms of accuracy, precision, and recall. YOLOv8 with ByteTrack and Supervision achieves an overall accuracy of 94% detection rate for concrete cracks and spalls, even under challenging conditions. Real-time deployment is achieved on a high-performance computing platform, allowing efficient and timely monitoring of concrete structures. The proposed method demonstrates its potential as a valuable tool for infrastructure management and maintenance, enabling early detection of concrete cracks and spalls. By promptly identifying such defects, necessary repair and reinforcement measures can be implemented, preventing further deterioration and ensuring the safety and longevity of civil infrastructure. The high detection performance of the proposed approach, achieving excellent precision, recall, and F1 score for both crack and spall detection. Our system's overall average precision of 93% indicates its accuracy in detecting concrete cracks and spalls. Additionally, the system exhibits fast real-time processing with an inference speed of 50fps and is highly robust in handling diverse concrete crack and spall scenarios. Future work may focus on expanding the dataset, exploring the integration of additional detection algorithms, and evaluating system performance in large-scale applications.