AI Floorboard Sorting with Cascade-RCNN Defect Detection
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Solution Overview
Problem
Traditional manual visual inspection of PVC floorboards for defects is inefficient, costly, and prone to variability due to worker fatigue and differing criteria, leading to low inspection speed, unstable accuracy, and high labor costs.
Innovation Solution
An automatic floorboard sorting method using artificial intelligence trained with a CASCADE-RCNN algorithm to identify defects in both color and black-and-white images, with iterative upgrading through sampling inspection, enabling efficient and accurate quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual visual inspection is used, then inspection cost is high and inspection speed is low, but the method is simple to implement
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated computer vision system. The CASCADE-RCNN algorithm processes images of floorboards to automatically detect defects, eliminating the need for manual visual screening and its associated labor intensity and speed limitations.
Solution Approach 2:
The patent creates a digital copy of the inspection process through image capture and algorithmic analysis. Instead of direct manual inspection, the system captures images and uses the CASCADE-RCNN model to replicate and execute the inspection function, enabling consistent automated detection.
2Reliability
If manual inspection is used, then labor cost is high, but the inspection criteria are uniform and stable
Solution Approach 1:
The patent substitutes human judgment with the CASCADE-RCNN algorithm, which provides consistent and uniform inspection criteria. The algorithm processes each image through the same trained model, eliminating the variability in human assessment while maintaining high accuracy through extensive training data.
Solution Approach 2:
The patent implements a feedback mechanism where inspection results are continuously refined. The system processes images through the CASCADE-RCNN model, and the results can be used to retrain and improve the algorithm, ensuring continuously improving accuracy and consistency in defect detection.
3Productivity
If manual inspection is used, then inspection speed is low, but the equipment cost is low
Solution Approach 1:
The patent replaces manual inspection with an automated image processing system that uses the CASCADE-RCNN algorithm. This substitution dramatically increases inspection speed by eliminating human processing time while the computational cost of the algorithm provides a cost-effective automated solution.
Solution Approach 2:
The patent creates a digital inspection system that copies and executes the inspection function through image capture and algorithmic analysis. This digital copy enables rapid processing of multiple floorboards without the physical limitations of manual inspection, significantly boosting productivity.
4Reliability
If manual inspection is used, then the inspection process is simple, but different workers have different criteria leading to unstable results
Solution Approach 1:
The patent substitutes variable human judgment with the fixed CASCADE-RCNN algorithm, which applies consistent inspection criteria to every floorboard. The algorithm's trained parameters ensure uniform detection standards across all inspections, eliminating the inconsistency caused by different workers' subjective criteria.
Solution Approach 2:
The patent uses parameter changes in the CASCADE-RCNN algorithm to adapt to different defect types and conditions. The model processes images through multiple stages with adjusted parameters, allowing it to maintain consistent and reliable detection across various defect scenarios while providing uniform criteria application.
Data Source
AI summary
The present disclosure belongs to a sorting method, and specifically relates to an automatic floorboard sorting method. An automatic floorboard sorting method includes the following steps: step 1: a training stage: training an artificial intelligence so that defects of floorboards in a black-and-white image and a color image can be automatically identified by the artificial intelligence; step 2: a using stage: using the artificial intelligence obtained by training in step 1 to perform identification, and performing sampling inspection to continuously iteratively upgrade the artificial intelligence. The present invention has the outstanding effects that an effective identification algorithm is formed by artificial intelligence training, and the algorithm is then used to carry out intelligent identification, so that the identification efficiency is high; the identification effect is good; the marginal cost is low; and it is conductive to the quality control for a floorboard finished product.