Complex Crack Detection of Postearthquake Building Using a Optimzied YOLOv4 Model

At present, the manual survey based traditional building damage detection highly depends on the prior experience and subjective judgment of the professional inspector. To realize the intelligent detection of building crack, this study proposes a crack detection method using a modified YOLOv4 based on the improvement of focal loss and adding attention modules. Taking consideration of both uneven positive crack and negative non-crack samples as well as different training coefficients in the loss function, the YOLOv4 model was deeply trained. To solve the inapplicability of traditional evaluation metrics for complex regional cracks, a refined evaluation metric is proposed. The proposed method achieved efficient and accurate detection of complex building cracks as large as 69.18%.