Deep learning-based spillway accumulation body blockage identification method
Through the deep learning-based image segmentation model, the risk of blockage of spillway accumulations is identified, which solves the problems of delay in identifying dangerous situations and low manual inspection efficiency in the existing technology, and achieves rapid and accurate blockage detection and engineering management automation.
CN119649095APending Publication Date: 2025-03-18ZHEJIANG INST OF HYDRAULICS & ESTUARY
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Patent Information
- Application Number
- CN202411669762.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-21
- Publication Date
- 2025-03-18
AI Technical Summary
Technical Problem
The prior art is difficult to quickly and accurately identify the danger of blockage of spillway accumulations, resulting in delayed reporting of dangerous situations, and low manual inspection efficiency and high cost.
Method used
The image segmentation model based on deep learning is used to simulate the spillway blockage through indoor experiments, an image data set is established, and the improved DeepLabV3+ model is used, combined with the MobileNetV2 network, to identify whether there is blockage in the image.
Benefits of technology
It realizes rapid and flexible detection of spillway accumulation blockage hazards, reduces the cost and error rate of manual inspection, and improves the automation level of engineering management.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a spillway accumulation body blockage identification method based on deep learning, and belongs to the technical field of hydraulic engineering safety detection and intelligent operation and maintenance. According to the invention, a possible rock block accumulation condition is simulated through an indoor experiment, and a set of image acquisition system is established to shoot spillway discharge chute blockage caused by rock blocks in different forms. Besides, an improved DeepLabV3 + model is adopted as an image semantic segmentation algorithm, the improved model can meet the detection requirements in efficiency and precision, and a model performance analysis experiment shows the effectiveness of the established method. Furthermore, the trained model is integrated into an open platform, so that the model is promoted to be integrated into the equipment terminal to realize automatic detection. The method provided by the invention is mainly applied to an engineering operation and maintenance stage, and a tedious process of traditional manual inspection is avoided.
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