国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

To address the problems of low automatic detection accuracy and inaccurate target positioning of urban drainage pipeline defects, an improved YOLOv8 drainage pipeline defect detection model is proposed. This model introduces receptive field attention convolution into the baseline model and constructs the C2F_RFAConv module to enhance the model's ability to extract defect features through interactive adaptive learning of spatial receptive fields and convolutions. Additionally, a hybrid attention high-order and-low-order feature fusion network is proposed, which effectively fuses the low-order and high-order features of three different scales output by the backbone and neck, enhancing the model's ability to learn the global contextual information of the image. The Inner-MPDIoU loss function is designed by comprehensively analyzing factors affecting bounding box regression, such as overlap, center point distance, and width-height deviation. This function enables the model to adapt to defect detection tasks of different sizes and improves the positioning accuracy of the defect target boundary box. Experimental validation shows that the improved model achieves an average detection accuracy of 93.9%, which is a 3.7% increase compared to the baseline model; the missed detection rate and false detection rate are reduced to 9.1% and 17.6%, representing decreases of 3.2% and 2.7%, respectively, compared to the baseline model.