Neural Network Defect Detection for Precision Chemical Application
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Solution Overview
Problem
Current computer vision techniques are inadequate for accurately detecting and localizing defects on large or hard-to-reach physical objects, such as agricultural fields and industrial products, leading to inefficient application of chemical treatments and potential environmental harm.
Innovation Solution
A computer-implemented method using a pre-trained machine learning model and a trained neural network to analyze surface images, identifying damage indices and generating control data for precise treatment application, with the neural network trained on annotated surface images to differentiate damaged areas and provide granular property values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision techniques are used to detect defects on large physical objects, then the detection process becomes computationally intensive and time-consuming, but the measurement precision and reliability of defect detection remain insufficient
Solution Approach 1:
The neural network model is pre-trained on a large dataset of annotated surface images containing various defects. This preliminary training action enables the model to learn defect patterns and characteristics in advance, so that during actual detection operations, the model can quickly and accurately identify defects without requiring extensive real-time computation, thus resolving the contradiction between detection accuracy and time consumption
Solution Approach 2:
Instead of processing raw images directly during detection, the system uses a pre-trained neural network model that has learned to represent defect patterns. The model creates an optimized computational copy of defect detection knowledge, allowing rapid inference on new images while maintaining high accuracy, thereby reducing detection time without sacrificing measurement precision
2Reliability
If chemical products are applied broadly to treat damaged areas on physical objects, then treatment coverage is ensured, but chemical usage increases and environmental harm worsens
Solution Approach 1:
The system applies chemical treatments locally only to detected defect areas rather than uniformly across the entire surface. The neural network precisely localizes defects and generates treatment instructions for specific regions, enabling targeted application of chemical products. This ensures treatment effectiveness for actual defects while minimizing unnecessary chemical usage and reducing environmental harm
Solution Approach 2:
The system enables precise, automated treatment application based on self-detected defect locations. The neural network model autonomously identifies defects and generates corresponding treatment instructions, allowing the treatment system to service only the necessary areas without human intervention or broad-spectrum application, thereby reducing chemical usage while maintaining treatment reliability
3Productivity
If manual inspection methods are used to identify defects on physical objects, then flexibility and adaptability are maintained, but productivity and detection accuracy are limited
Solution Approach 1:
The system replaces manual mechanical inspection with an automated neural network-based computer vision system. The neural network model processes surface images automatically, identifying and localizing defects with high precision and speed. This substitution of mechanical/manual inspection with intelligent automated processing simultaneously improves both productivity (detection throughput) and measurement precision (defect localization accuracy)
Solution Approach 2:
The neural network model transforms the detection process by changing key parameters: it processes images at high speed to improve productivity, while simultaneously achieving superior defect localization accuracy through learned feature representations. The model adjusts detection sensitivity and precision parameters based on the trained data, enabling both high throughput and high accuracy that manual inspection cannot achieve
Data Source
AI summary
The present disclosure relates to image processing or computer vision techniques. A computer-implemented method is provided for determining a damage status of a physical object, the method comprising the steps of receiving a surface image of the physical object; and providing a pre-trained machine learning model to derive property values from the received surface map, wherein each property value is indicative of a damage index at a respective location, wherein the property values are preferably usable for monitoring and/or controlling a production process of the physical object. In this way, it is possible to reliably identify local defects and ensure that it is accurate enough to apply the chemical products in suitable amounts.