Dam Defect Image Description With Local Self-Attention
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Solution Overview
Problem
Existing methods for inspecting dam defects using video data from drones and mobile cameras face challenges in directly processing video data due to compression and coding, leading to loss of feature information and reduced accuracy in generating defect descriptions.
Innovation Solution
A time-series image description method using a local self-attention mechanism, involving frame sampling, convolutional neural networks, Transformer networks with variable self-attention, and LSTM networks with local attention, to dynamically establish contextual relations and ensure each word corresponds to a specific image frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video data is compressed and coded to save costs, then transmission efficiency is improved, but feature information is lost
Solution Approach 1:
The patent extracts key frames from video sequences before processing, performing the essential action of selecting representative images in advance. This preliminary frame selection preserves critical defect information while reducing the overall data volume that needs to be processed and transmitted, thus resolving the contradiction between transmission efficiency and feature information preservation
Solution Approach 2:
The patent extracts specific key frames containing defect information from the compressed video data. By taking out only the essential frames that contain meaningful defect characteristics, the system maintains feature information integrity while working with reduced data volumes, effectively addressing the information loss problem caused by compression
2Measurement precision
If all image frames are processed to ensure complete information, then accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the video sequence into segments and selects key frames from each segment rather than processing every frame. This segmentation approach maintains defect detection accuracy by ensuring representative frames are captured while significantly reducing the total number of frames processed, thus resolving the time-accuracy trade-off
Solution Approach 2:
The patent processes only a partial set of key frames rather than all frames in the video sequence. This partial action is sufficient to capture all necessary defect information while avoiding the excessive processing time that would result from analyzing every single frame, effectively balancing accuracy and efficiency
3Measurement precision
If global self-attention is used to capture all contextual relations, then feature relation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent employs local self-attention mechanisms that focus computational resources on locally relevant regions and frames rather than computing global attention across the entire sequence. This local quality approach maintains accurate feature relation capture for defect-related areas while reducing overall computational complexity by ignoring irrelevant distant regions
Solution Approach 2:
The patent segments the attention computation into local regions and time windows, processing only relevant portions of the image sequence for each defect detection task. This segmentation of the attention mechanism reduces the quadratic computational complexity of global self-attention while preserving the essential contextual relations needed for accurate defect description
Data Source
AI summary
A time-series image description method for dam defects based on local self-attention mechanism is provided, including: performing frame sampling on an input time-series image of dam defect, extracting a feature sequence using a convolutional neural network and using the sequence as an input to a self-attention encoder, where the encoder includes a Transformer network based on a variable self-attention mechanism that dynamically establishes contextual feature relations for each frame; generating description text using a long short term memory (LSTM) network based on a local attention mechanism to enable each word predicted to be feature related to an image frame, improving text generation accuracy by establishing a contextual dependency between image and text. A dynamic mechanism is added to the present application for calculating the global self-attention of image frames, and LSTM networks with added local attention directly establish the correspondence between image and text modal data.