Insulator Orientation Detection Using Deep Neural Networks
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Solution Overview
Problem
Existing deep learning approaches for identifying insulator orientation in overhead line images are inaccurate, posing safety risks and inefficiencies in manual inspections, and are not feasible due to accessibility constraints.
Innovation Solution
A multi-stage classification approach using a deep neural network trained with augmented insulator images to detect and correct the orientation of insulators, generating a rectified image where the insulators are substantially horizontal or vertical, facilitating easier defect identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of overhead line insulators is performed, then inspection personnel can directly assess insulator condition, but safety risks increase and inspection efficiency decreases
Solution Approach 1:
The patent creates a digital copy (image) of the insulator and processes it through a neural network to extract orientation and condition information. Instead of requiring personnel to physically inspect the insulator, the system captures an image and generates a rectified version that preserves all necessary visual information for assessment, eliminating safety risks while maintaining assessment accuracy
Solution Approach 2:
The patent replaces the mechanical/physical inspection process with an automated image processing system. The neural network-based orientation detection and image rectification system substitutes human visual inspection, eliminating the need for personnel to physically access hazardous locations while maintaining or improving inspection quality
2Measurement precision
If manual inspection of overhead line insulators is performed, then detailed assessment can be conducted, but time consumption increases and cost-effectiveness decreases
Solution Approach 1:
The system performs self-service by automatically detecting insulator orientation and rectifying images without human intervention. The neural network autonomously processes images, corrects orientations, and prepares outputs for assessment, eliminating the time-consuming manual inspection process while maintaining detection accuracy
Solution Approach 2:
The patent replaces slow manual inspection with rapid automated image processing. The neural network-based system processes images much faster than human inspectors can visually examine insulators, dramatically improving inspection speed and cost-effectiveness while preserving defect detection capabilities
3Extent of automation
If existing deep learning approaches are used for insulator identification, then insulator detection is achieved, but orientation determination accuracy is insufficient
Solution Approach 1:
The patent segments the insulator identification task into two distinct stages: first detecting the insulator's presence and location, then separately determining its orientation. This segmentation allows each stage to be optimized independently, with the orientation detection stage specifically designed to achieve high angular precision that generic object detection models cannot provide
Solution Approach 2:
The patent performs preliminary orientation detection and image rectification before final defect assessment. By pre-processing images to correct orientations using the neural network's angular predictions, the system ensures that subsequent analysis works with properly oriented images, thereby improving overall orientation determination accuracy
Data Source
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AI summary
Systems, methods, and computer-readable media are described for determining the orientation of a target object in an image and iteratively reorienting the target object until an orientation of the target object is within an acceptable threshold of a target orientation. Also described herein are systems, methods, and computer-readable media for verifying that an image contains a target object.