目的 水岸线既是水利行业视频监控分析的基础，也是无人水面艇实现自主航行的关键。现有的许多水岸线检测的图像识别方法，不仅无法克服水面波纹、水面倒影等因素的影响，而且不具有适应性，无法同时适用于多个水岸场景分析。为此，本文采用多个复杂的水岸场景图像，训练了用于水岸分割的Deeplab v3+网络，并综合考虑分割性能和计算速度，对Deeplab v3+进行简化与改进，提出了基于改进的Deeplab v3+分割水面图像提取水岸线的检测方法。方法 采集不同水岸场景图像作为训练及验证图集，并利用伽马函数扩充样本；接着修改Deeplab v3+网络，对xception结构进行微调，同时在decoder时多增加一路低级特征（low-level feature），增加特征信息；然后依据图像信息设置损失权重系数，设置可视化参数，基于改进的Deeplab v3+网络针对自己的数据集进行训练。利用训练好的PB模型在Linux操作系统调用TensorFlow的C++接口对测试图像进行区域分割。最后基于提取出的水面区域通过边缘检测算子检测水岸线，将水岸线叠加到原图。结果 本文采集了不同光照强度、不同波纹程度以及不同阴影程度的水面图像进行水岸线检测实验，并与现有算法进行比较。实验结果表明本文算法可以在不同的水岸图像中检测出较为清晰完整的水岸线，准确率达93.98%，实时性达到8帧/s。结论 本文算法能克服水岸边缘严重不规则、不同水岸场景差异大和复杂水岸场景中光照、波纹、倒影等因素的干扰，提升水岸图像分割准确度及效率，检测出轮廓清晰完整的水岸线，服务于水利行业的智能监控分析。

Objective The shoreline is not only the basis of analysis in video surveillance in the water industry but also the key to autonomous navigation of unmanned surface boats. Many scholars have proposed shoreline detection methods. However, many existing shoreline detection algorithms utilize traditional image recognition methods for image segmentation using several features of the water surface and the ground. When dealing with different scenes, the parameters must be adjusted, but this is unsuitable for complex scenes. Traditional detection methods cannot overcome the influence of various factors, such as water surface ripple and reflection, and are unadaptable because they cannot be applied to the simultaneous analysis of multiple shoreline scenes. In this study, the Deeplab v3+ network for shoreline segmentation is trained by applying several complex shoreline scene images provided by the Chengdu River Chief's Office and self-photographed images. We simplify the Deeplab v3+ network to improve the performance and speed of segmentation. Then, on the basis of the improved Deeplab v3+, we segment the water surface image and propose a method to extract the shoreline by using the segmented image to achieve automatic shoreline extraction. Method First, images of different waterfront scenes are collected for training and verification sets. To further improve the generalization capability of the network, we use the gamma function to process 10 photos captured in different complex scenes and simulate different lighting situations in the same scene. We add 20 processed images to the verification set to expand the sample. After performing comparative experiments on various semantic segmentation networks, the Deeplab v3 + network is selected for modification, and the Xception structure is fine-tuned to reduce the number of network layers and increase the speed. Meanwhile, a low-level feature is added to the decoder to increase the feature information. Consequently, the time consumption is reduced without affecting the accuracy of the algorithm. Comparison of the modified network with the original network indicates that the modified network improves computational efficiency when the accuracy is basically unchanged. Then, according to the image information, we set the loss weight coefficient and visualization parameters to train data under the improved Deeplab v3+. Second, using the Linux operating system with the C++ interface of TensorFlow, the test image is segmented under the trained PB model. Finally, the waterfront line is detected by the edge detection operator on the basis of the extracted water surface region. The extracted water shoreline is expanded and superimposed on the original image for convenient observation. Result Waterfront detection experiments are performed on the collection of water surface images with different illumination intensities, degrees of corrugation, and shadows. Representative waterfront images are selected and compared using waterfront algorithms proposed by scholars, such as Iwahash et al., Bao et al., and Peng et al.. Experimental results show that only the proposed algorithm can completely process complex scenes on land and water surfaces and accurately detect clear and complete waterfront lines in different waterfront images. Furthermore, the real-time performance of the algorithm can reach 8 frame/s. Compared with the speed of other algorithms, the speed of the proposed algorithm is increased by nearly five times, and its accuracy is 93.98%, indicating an improvement of nearly 20%. Conclusion The algorithm can overcome the following situations:severely irregular waterfront edges, large difference in waterfront scenes, and interference of light, ripple, reflection, and other factors in complex waterfront scenes. In practical application, the algorithm achieves automatic shoreline extraction without artificial configuration and tuning. In addition, the accuracy and efficiency of waterfront image segmentation are improved, and a clear and complete waterfront line is detected for intelligent monitoring and analysis in the water conservancy industry. The algorithm requires a very large number of samples. However, the current sample is far from being sufficient. In the future, the number of samples in different scenarios must be increased to enhance the generalization capability of the network and the applicability of the algorithm. The application of this method in Windows and the improvement of the algorithm's practicability are other future research directions.