Surface Detection of Solid Wood Defects Based on SSD Improved with ResNet

Yutu Yang,Zhongkang Hu,Dong Jiang,Honghong Wang

doi:10.3390/f12101419

Yutu Yang, Zhongkang Hu + Show 2 more

Open Access

https://doi.org/10.3390/f12101419

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Journal: Forests	Publication Date: Oct 18, 2021
Citations: 24	License type: CC BY 4.0

Affiliation: Nanjing Forestry University, Yonyou (China), Fujitsu (China)

Abstract

Due to the lack of forest resources in China and the low detection efficiency of wood surface defects, the output of solid wood panels is not high. Therefore, this paper proposes a method for detecting surface defects of solid wood panels based on a Single Shot MultiBox Detector algorithm (SSD) to detect typical wood surface defects. The wood panel images are acquired by an independently designed image acquisition system. The SSD model included the first five layers of the VGG16 network, the SSD feature mapping layer, the feature detection layer, and the Non-Maximum Suppression (NMS) module. We used TensorFlow to train the network and further improved it on the basis of the SSD network structure. As the basic network part of the improved SSD model, the deep residual network (ResNet) replaced the VGG network part of the original SSD network to optimize the input features of the regression and classification tasks of the predicted bounding box. The solid wood panels selected in this paper are Chinese fir and pine. The defects include live knots, dead knots, decay, mildew, cracks, and pinholes. A total of more than 5000 samples were collected, and the data set was expanded to 100,000 through data enhancement methods. After using the improved SSD model, the average detection accuracy of the defects we obtained was 89.7%, and the average detection time was 90 ms. Both the detection accuracy and the detection speed were improved.

Highlights

As one of the emerging technologies, image processing technology has been widely used in wood detection [2,3]
The results show that the network structure used in this paper can improve the detection accuracy of surface defects of solid wood panels on the premise of reducing image processing time
The Single Shot MultiBox Detector algorithm (SSD) feature mapping layers are composed of a multi-layer convolutional neural network

Summary

Introduction

Image segmentation [10,11] and the feature extraction process are usually very difficult, since the natural texture of wood products is complex, and defect types are quite different. The application of deep learning methods [12,13,14] can improve the detection precision of surface defects of solid wood panels and achieve better detection performance on the premise of reducing the time of image processing. Yang [20] used a 3D laser sensor system to classify and identify surface defects of lumber and agri-crop straw-based panels by selecting insect holes and dents as detection objects and obtained the final classification accuracy of 94.67% after applying SVM. The results show that the network structure used in this paper can improve the detection accuracy of surface defects of solid wood panels on the premise of reducing image processing time

Image Acquisition and Environment Configuration

SSD Model

Loss Function

Deep Residual Network

Findings

Results