基于语义分割的船闸水位检测方法研究
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TH764

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江苏省交通运输科技项目(2017Y10)资助


Research on water level detection of ship lock based on semantic segmentation
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    摘要:

    针对船闸水位传感器精度易受水质影响、传统图像检测方法适应性差等问题,提出一种基于语义分割和由粗到精策略 的水位线检测方法,建立分段对照的标定模型计算水位。 结合水位线长程相关性的特点,设计条带状空洞空间金字塔池化模 块,针对分割边界模糊的问题,设计多路聚合上采样模块并引入在线困难样本挖掘,提升语义分割模型的分割精度。 采用改进 的语义分割模型在由原图像经压缩后的低分辨率图像中进行粗检测,分割图像中的水区域与非水区域,根据模型输出的掩膜图 得到水位线的粗检测结果,然后在原图像中裁剪粗检测水位线的邻域并采用该模型进行精检测,得到水位线的精检测结果。 建 立分段对照的标定模型,获取像素坐标与世界坐标之间的映射关系,根据水位线精检测结果计算水位。 在构建的水位图像数据 集上进行试验,结果表明:在验证集上,改进后的语义分割模型粗检测和精检测的平均交并比分别提升了 2. 58% 和 1. 98% ,水位 线精检测结果的平均像素误差为 1. 89 pixel,相比粗检测结果降低了 52. 3% ,以水尺人工观测的读数为基准,摄像机在 23 m 工 作距离下水位测量结果置信水平为 95% 的不确定度为 0. 026 m。 所提方法对晴天、阴天、雨天、雪天等多种室外环境具有良好 的适应性,为船闸水位检测提供了一种可行的方法。

    Abstract:

    In terms of the issues that the accuracy of ship lock water level sensor is easily affected by the water quality and the poor adaptability of the traditional image detection methods, a waterline detection method based on semantic segmentation and coarse-to-fine strategy is proposed, and the calibration model with subsection control points is established to calculate the water level. Considering the characteristics of long-range dependence of waterline, the strip atrous spatial pyramid pooling module is proposed. To address the problem of inaccurate segmentation boundary, the multi-path aggregation upsample module is proposed and online hard example mining is introduced to improve the segmentation accuracy of the model. The improved semantic segmentation model is used to perform coarse detection, separates water and non-water regions in the low-resolution image, which is compressed from the original image, and obtains the coarse detection result of the waterline according to the binary mask output from the model. Then, crops the neighborhood of the coarse detected waterline in the original image and performs fine detection to obtain the fine detection result of the waterline. Finally, establishes the calibration model with subsection control points to build the relationship between pixel coordinates and world coordinates, and calculates water level by fine detection result. The experiments are implemented on the constructed water level dataset. Experimental results show that the improved semantic segmentation model increases mIoU by 2. 58% and 1. 98% for coarse detection and fine detection, respectively. The average pixel error of the fine detection result is 1. 89 pixel, which is 52. 3% lower than the coarse detection result. With manual observation of the water gauge as the benchmark, when the working distance of the camera is 23 m, the uncertainty of the measurement result with a confidence level of 95% is 0. 026 m. The proposed method has well adaptability for a variety of outdoor environments such as sunny, cloudy, rainy and snowy days, which provides an available method for water level detection of ship lock.

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曹文卓,王太固,徐 兵,冯玉凯,王化明.基于语义分割的船闸水位检测方法研究[J].仪器仪表学报,2023,44(2):238-247

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  • 在线发布日期: 2023-07-07
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