Objective

2

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

2

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

2

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

2

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.