Water Resources and Power

The apparent diseases of hydraulic concrete structures are suffered from the uneven scale, low resolution and complex background interference, which brings the lower detection accuracy and efficiency in existing object detection algorithm. An improved YOLOv7 detection model is proposed. Firstly, the CBAM attention mechanisms is added to the three feature output layers of the backbone network to make networks pay more attention to target features from two dimensions of space and channel. Secondly, the path aggregation network(PAN) is replaced to the weighted bidirectional feature pyramid network(BiFPN) in the neck network, which further integrates the shallow position and the deep semantic information. CIoU is replaced by SIoU as the localization loss function, improving the accuracy of regression. Finally, the data is strengthened by means of generative adversarial network(GAN), and the detection effect is visualized. The experimental results show that the improved YOLOv7 model has faster convergence and higher classification accuracy, and the m_mAP value reaches 89.4%, which is 3.2% higher than that of YOLOv7. The detect effect is superior to other object detection algorithm, and the real-time detection of diseases is realized.