The inadequate visual environment reduces the accuracy of underwater bridge pier fracture detection. Consequently, this paper suggests enhancing the backbone of the Real-Time Detection Transformer(RT-DETR) model to serve as the backbone of the YOLOv8 model. This will be achieved by substituting the Faster Implementation of CSP Bottleneck with 2 convolutions(C2f) module with the Poly Kernel Inception(PKI) Block, which is composed of the PKI Module and Context Anchor Attention(CAA) Block. Its strong capability to distinguish cracks and background features enables accurate recognition of underwater bridge pier cracks. To provide data for detecting these cracks, the enhanced Unpaired Image to Image Translation(CycleGAN) network converts land-style bridge crack images to underwater-style fracture images. The proposed model achieved an F1 score of 0.85 and a mAP50 of 0.84. The real-time detection of underwater bridge fractures by the underwater robot was facilitated by the FPS index of 87.47, which optimizes the detection efficiency.

中文翻译：

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用于水下桥墩裂缝机器人检测的增强型实时检测变压器 （RT-DETR）


视觉环境不足降低了水下桥墩断裂检测的准确性。因此，本文建议增强实时检测转换器 （RT-DETR） 模型的骨干，作为 YOLOv8 模型的骨干。这将通过将 2 个卷积 （C2f） 模块替换为 Poly Kernel Inception（PKI） 模块来实现，该模块由 PKI 模块和上下文锚点注意力 （CAA） 模块组成。其强大的裂缝和背景特征区分能力使水下桥墩裂缝的准确识别成为可能。为了提供检测这些裂缝的数据，增强的未配对图像到图像转换 （CycleGAN） 网络将陆地式桥梁裂缝图像转换为水下式裂缝图像。所提出的模型获得了 0.85 的 F1 分数和 0.84 的 mAP50。FPS 指数为 87.47，有利于水下机器人对水下桥梁断裂的实时检测，优化了检测效率。