基于融合图像增强与改进YOLOv7算法的桥梁水下结构缺陷识别

BRIDGE UNDERWATER STRUCTURAL DEFECTS DETECTION BASED ON FUSION IMAGE ENHANCEMENT AND IMPROVED YOLOV 7

该文提出了基于水下机器人的桥梁水下结构缺陷自动识别方法。在浑水环境拍摄获取低质缺陷图像并进行数据增强,扩充数据集;针对浑水下低质图像,通过级联Water-Net水下图像增强算法作为输入端获取高质量图像。针对图像增强与目标检测不匹配所导致的抑制作用,利用极化自注意力(Polarized Self-Attention)模块保持增强图像的高分辨率输出,使图像增强与目标检测有效协同,提高检测精度。在此基础上,考虑低质图像数据标注难免包含低质量示例,使用WIoU损失函数替换原YOLOv7-tiny网络模型中的损失函数,提高模型泛化性能。实验结果表明:改进后的网络模型相比原网络,在保持缺陷识别精度的同时,图片对比度高,细节更加清晰,视觉效果优良,漏检与误判情况得到明显改善。

This study presents a new method for automatic identification of structural defects by using ROV.First,low-quality fault photos are obtained under muddy water to expand the dataset.Next,the cascaded Water-Net underwater image improvement method is used to create high-quality images from low-quality photos.The polarized self-attention module is employed to preserve the high resolution output of the improved image,thus allowing the image enhancement and target detection to work together efficiently and enhance the detection accuracy,in order to counteract the hindrance caused by the discrepancy between image enhancement and target detection.Given that low-quality image data annotation would necessarily contain low-quality examples,the WioU loss function is used to replace the loss function in the original YOLOv7-tiny network model to improve generalization performance.The experimental results reveal that,compared with the original network,the revised network model retains defect recognition accuracy while retaining high picture contrast,sharper details,and superb visual effects,as well as dramatically improving missed detections and misjudgments.