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

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.

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A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning

Contrast Media & Molecular Imaging ◽

10.1155/2021/3474921 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Yi Lv ◽

Zhengbo Yin ◽

Zhezhou Yu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Target Detection ◽

Detection Efficiency ◽

False Positive Rate ◽

Remote Sensing Image ◽

Detection Algorithm ◽

Detection Accuracy ◽

Data Set ◽

Detection Effect

In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segmentation detection are designed. The data set used in the experiment comes from GoogleEarth, and there are 6 types of objects: airplanes, boats, warehouses, large ships, bridges, and ports. Training set, verification set, and test set contain 73490 images, 22722 images, and 2138 images, respectively. It is assumed that the number of detected positive samples and negative samples is A and B, respectively, and the number of undetected positive samples and negative samples is C and D, respectively. The experimental results show that the precision-recall curve of DFS for six types of targets shows that DFS has the best detection effect for bridges and the worst detection effect for boats. The main reason is that the size of the bridge is relatively large, and it is clearly distinguished from the background in the image, so the detection difficulty is low. However, the target of the boat is very small, and it is easy to be mixed with the background, so it is difficult to detect. The MAP of DFS is improved by 12.82%, the detection accuracy is improved by 13%, and the recall rate is slightly decreased by 1% compared with YOLOv2. According to the number of detection targets, the number of false positives (FPs) of DFS is much less than that of YOLOv2. The false positive rate is greatly reduced. In addition, the average IOU of DFS is 11.84% higher than that of YOLOv2. For small target detection efficiency and large remote sensing image detection, the DFS algorithm has obvious advantages.

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NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video

Computational Intelligence and Neuroscience ◽

10.1155/2021/7018035 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Jiuwu Sun ◽

Zhijing Xu ◽

Shanshan Liang

Keyword(s):

Real Time ◽

Clustering Algorithm ◽

Detection Efficiency ◽

Rapid Development ◽

Outdoor Environment ◽

Detection Methods ◽

Detection Accuracy ◽

Single Shot ◽

Ship Detection ◽

Real Time Detection

With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real-time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD-SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD-SSD uses visual images captured by surveillance cameras to achieve real-time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K-means clustering algorithm, the Intersection-over-Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R-CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real-time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.

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Detection of wood surface defects based on improved YOLOv3 algorithm

BioResources ◽

10.15376/biores.16.4.6766-6780 ◽

2021 ◽

Vol 16 (4) ◽

pp. 6766-6780

Author(s):

Baogang Wang ◽

Chunmei Yang ◽

Yucheng Ding ◽

Guangyi Qin

Keyword(s):

Real Time ◽

Wood Surface ◽

Surface Defects ◽

Detection Efficiency ◽

Online Detection ◽

Network Parameter ◽

Baseline Model ◽

Generalization Ability ◽

The Mean ◽

Residual Block

For the detection of wood surface defects, a convolutional neural network has a low detection efficiency and insufficient generalization ability, so it does not meet the requirements of online detection. Aiming to solve the above problems, the YOLOv3 baseline model, which has the advantage of multi-objective dynamic detection, was improved and applied to the online detection of wood surface defects. To solve the problem of the poor generalization ability of the network, GridMask was used to enhance the data and improve the robustness of the network. In order to solve the problem of the considerable amount of network parameter calculations and insufficient real-time performance, the residual block of the backbone network was changed to a Ghost block structure to achieve a lightweight model. Finally, the confidence loss function of the network was improved to reduce the influence of simple samples and negative samples on model convergence. The experimental results showed that, compared with the original network, the improved algorithm increased the mean average precision by 5.73% and the detection speed was increased to 28 frames per second (an increase of 11), which met the requirements for real-time industrial detection.

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Recent Patents on Detection Device of Surface Defects on Bearing Balls

Recent Patents on Mechanical Engineering ◽

10.2174/2212797614666210310155133 ◽

2021 ◽

Vol 14 ◽

Author(s):

Chengyi Pan ◽

Junhong Tang ◽

Guanqun Cao ◽

Bingtao Hu

Keyword(s):

Surface Defects ◽

Detection Efficiency ◽

High Surface ◽

Detection Accuracy ◽

High Quality ◽

High Surface Quality ◽

Existing Problems ◽

Advantages And Disadvantages ◽

Surface Detection ◽

Scope Of Application

Background: With the continuous pursuit of high-quality and high-precision bearings in industrial production, the processing accuracy of bearing balls is increasing, and high surface quality has become the most basic requirement for high-quality bearing balls. Studying the working principle of detection device of surface defects on bearing balls is beneficial to improve the surface accuracy of bearing balls and the quality of bearings. Objective: To meet the increasing requirements for measurement accuracy and efficiency of surface detection on bearing balls, the devices and methods of surface detection on bearing balls have been improved constantly. Methods: This paper reviews various representative patents related to the detection device of surface defects on bearing balls. The advantages and disadvantages of the patents are analyzed. Results: Through tracing the characteristics of different types of the detection device of surface defects on bearing balls, the main existing problems in the current are concluded and analyzed, such as low detection accuracy, low detection efficiency, and limited scope of application, etc. The future development of patents on detection device of surface defects on bearing balls is discussed. Conclusion: The optimization and development of detection device of surface defects on bearing balls are beneficial to improve the detection accuracy and efficiency of surface defects on bearing balls, and are conducive to expand the scope of application of the detection devices. More related patents on detection device of surface defects on bearing balls will be invented.

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Rice-Field Mapping with Sentinel-1A SAR Time-Series Data

Remote Sensing ◽

10.3390/rs13010103 ◽

2020 ◽

Vol 13 (1) ◽

pp. 103

Author(s):

Lena Chang ◽

Yi-Ting Chen ◽

Jung-Hua Wang ◽

Yang-Lang Chang

Keyword(s):

Time Series ◽

Detection Efficiency ◽

Experimental Results ◽

The Other ◽

Series Data ◽

Support Vector ◽

Time Interval ◽

Detection Accuracy ◽

Data Set ◽

Decision Method

This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.

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Modeling of Influential Parameters on Bending Strength of Solid Wood Panels Perpendicular to the Grain

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1208/1/012020 ◽

2021 ◽

Vol 1208 (1) ◽

pp. 012020

Author(s):

Damir Hodžić ◽

Nedim Hurem ◽

Atif Hodžić

Keyword(s):

Mathematical Model ◽

Bending Strength ◽

Solid Wood ◽

Breaking Force ◽

Experimental Examination ◽

Wood Panel ◽

Marginal Areas ◽

Wood Panels ◽

Definition Of ◽

Influential Parameters

Abstract The paper presents the results of modeling the bending strength of wood. During the experimental examination and definition of the model, solid wood was taken, where the bending was performed perpendicular to the grain. The experiment was done with thirteen replications and the input values that varied at three levels were wood density and board thickness. The thirteen-repetition experiment also involved four repetitions in the marginal areas, so two more wood densities and two board thicknesses had to be taken. The experimental measurement was performed in the laboratory of the Technical Faculty Bihać. Based on the experimental results, a sufficiently adequate mathematical model of the breaking force of a solid wood panel perpendicular to the grain is obtained.

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Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid

Applied Sciences ◽

10.3390/app11188692 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8692

Author(s):

Chansoo Park ◽

Sanghun Lee ◽

Hyunho Han

Keyword(s):

Deep Learning ◽

Detection Efficiency ◽

Rapid Development ◽

Extraction Process ◽

General Purpose ◽

Learning Technologies ◽

Detection Accuracy ◽

Data Set ◽

Small Parameters ◽

Feature Pyramid

Convolutional-neural-network (CNN)-based methods are continuously used in various industries with the rapid development of deep learning technologies. However, an inference efficiency problem was reported in applications that require real-time performance, such as a mobile device. It is important to design a lightweight network that can be used in general-purpose environments such as mobile environments and GPU environments. In this study, we propose a lightweight network efficient shot detector (ESDet) based on deep training with small parameters. The feature extraction process was performed using depthwise and pointwise convolution to minimize the computational complexity of the proposed network. The subsequent layer was formed in a feature pyramid structure to ensure that the extracted features were robust to multiscale objects. The network was trained by defining a prior box optimized for the data set of each feature scale. We defined an ESDet-baseline with optimal parameters through experiments and expanded it by gradually increasing the input resolution for detection accuracy. ESDet training and evaluation was performed using the PASCAL VOC and MS COCO2017 Dataset. Moreover, the average precision (AP) evaluation index was used for quantitative evaluation of detection performance. Finally, superior detection efficiency was demonstrated through the experiment compared to the conventional detection method.

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Research on Real-Time Infrared Image Fault Detection of Substation High-Voltage Lead Connectors Based on Improved YOLOv3 Network

Electronics ◽

10.3390/electronics10050544 ◽

2021 ◽

Vol 10 (5) ◽

pp. 544

Author(s):

Qiwei Xu ◽

Hong Huang ◽

Chuan Zhou ◽

Xuefeng Zhang

Keyword(s):

Fault Detection ◽

Real Time ◽

Network Model ◽

High Voltage ◽

Detection Efficiency ◽

Recognition Performance ◽

Infrared Image ◽

Image Features ◽

Detection Accuracy ◽

Data Set

Currently, infrared fault diagnosis mainly relies on manual inspection and low detection efficiency. This paper proposes an improved YOLOv3 network for detecting the working state of substation high-voltage lead connectors. Firstly, dilated convolution is introduced into the YOLOv3 backbone network to process low-resolution element layers, so as to enhance the network’s extraction of image features, promote function propagation and reuse, and improve the network’s recognition performance of small targets. Then the fault detection model of the infrared image of the high voltage lead connector is created and the optimal infrared image test data set is obtained through multi-scale training. Finally, the performance of the improved network model is tested on the data set. The test results show that the improved YOLOv3 network model has an average detection accuracy of 84.26% for infrared image faults of high-voltage lead connectors, which is 4.58% higher than the original YOLOv3 network model. The improved YOLOv3 network model has an average detection time of 0.308 s for infrared image faults of high-voltage lead connectors, which can be used for real-time detection in substations.

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A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

Journal of Neurosurgery Spine ◽

10.3171/2019.6.spine19463 ◽

2019 ◽

Vol 31 (6) ◽

pp. 844-850 ◽

Cited By ~ 1

Author(s):

Kevin T. Huang ◽

Michael A. Silva ◽

Alfred P. See ◽

Kyle C. Wu ◽

Troy Gallerani ◽

...

Keyword(s):

Computer Vision ◽

Feature Detection ◽

High Accuracy ◽

Detection Accuracy ◽

Data Sets ◽

Visual Words ◽

Fusion Systems ◽

Kaze Feature ◽

Applications Of Machine Learning ◽

Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

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Untersuchungen zum Diffusionswiderstand von Holzwerkstoffen | Diffusion resistance of timber materials

Schweizerische Zeitschrift fur Forstwesen ◽

10.3188/szf.2005.0100 ◽

2005 ◽

Vol 156 (3-4) ◽

pp. 100-103

Author(s):

Rudolf Popper ◽

Peter Niemz ◽

Gerhild Eberle

Keyword(s):

Relative Humidity ◽

Water Vapour ◽

Middle Lamella ◽

Solid Wood ◽

Diffusion Resistance ◽

Water Vapour Diffusion ◽

Wood Panels ◽

Vapour Diffusion ◽

Wet Climate ◽

Panel Thickness

The water vapour diffusion resistance of timber materials were tested in a wet climate (relative humidity ranging from 100%to 65% at 20 °C) and in a dry climate (relative humidity ranging from 0% to 65% and from 0% to 35% at 20 °c) with variation by relative humidity and vapour pressure gradient. The diffusion resistance of multilayer solid wood panels lies under or within the range of the solid wood (spruce), tending even to a lower range. This can be attributed to the loosely inserted middle lamella of the used solid wood panels, which were not correctly glued by the manufacturer. The diffusion resistance of the solid wood panels increases with decreasing moisture content and decreasing panel thickness, as well as with increasing water vapour gradient from 818 to 1520 Pa. There were clear differences between the tested timber materials. The diffusion resistance of particle composites is strongly dependent on the specific gravity. Due to laminar particles OSBs(Oriented Strand Boards) have a larger diffusion resistance than chipboards. The water vapour diffusion resistance of OSBs lies within the range of plywood.

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