基于深度学习的木材优选锯视觉检测算法

doi:10.11707/j.1001-7488.20201214

Abstract

Abstract:

Objective: For the purposes of playing full value of wood, increasing the timber yield and improving the automatic level of wood processing industry, a new method for automatic optimizing cross-cut saw based on deep learning algorithm was proposed in this paper. Method: Pretrained defect detection network (Net.1) and grade classification network (Net.2) were obtained based on enough samples. Images of timber were captured by the vision sensor, the defect position on image could be confirmed based on Net. 1, while the grade position could be confirmed based on Net. 2. Final physical position could be determined based on the homography relationships between image plane and physical plane. Cutting list could also be obtained. Result: The experiment in our detailed conditions showed that the time of defect detection was 123 ms (per image), while the accuracy was 95.8% (per image). The time of grade classification was 55 ms (per image), while the accuracy was 97.1% (per image). The average time of detection was 86 ms (per image), while the average accuracy was 96.5% (per image). Conclusion: Our new method presented many advantages, such as a high operation speed, a high recognition efficiency and a strong robustness. The algorithm could improve the traditional optimizing cross-cut saw with a high automation and could also meet the requirements of real-time and accurate detection in wood processing.

Key words: deep learning, optimizing cross-cut saw, wood processing, defect detection, computer vision

CLC Number:

S777

Mingwei Shao,Junyu Dong. A New Algorithm for Automatic Optimizing Cross-Cut Saw Based on Deep Learning Algorithm[J]. Scientia Silvae Sinicae, 2020, 56(12): 123-129.

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缺陷检测Defect detection		等级分类Grade classification		平均指标Average result
单幅图像时间 Time per image/ms	正确率 Accuracy(%)	单幅图像时间 Time per image/ms	正确率 Accuracy(%)	平均时间 Average time/ms	平均正确率 Average accuracy(%)
123	95.8	55	97.1	86	96.5

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A New Algorithm for Automatic Optimizing Cross-Cut Saw Based on Deep Learning Algorithm

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