scholarly journals YOLOv3-Lite: A Lightweight Crack Detection Network for Aircraft Structure Based on Depthwise Separable Convolutions

2019 ◽  
Vol 9 (18) ◽  
pp. 3781 ◽  
Author(s):  
Yadan Li ◽  
Zhenqi Han ◽  
Haoyu Xu ◽  
Lizhuang Liu ◽  
Xiaoqiang Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Crack Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Aviation Industry ◽  
Detection Accuracy ◽  
Aircraft Structure ◽  
Aircraft Structures ◽  
Backbone Network ◽  
Feature Pyramid

Due to the high proportion of aircraft faults caused by cracks in aircraft structures, crack inspection in aircraft structures has long played an important role in the aviation industry. The existing approaches, however, are time-consuming or have poor accuracy, given the complex background of aircraft structure images. In order to solve these problems, we propose the YOLOv3-Lite method, which combines depthwise separable convolution, feature pyramids, and YOLOv3. Depthwise separable convolution is employed to design the backbone network for reducing parameters and for extracting crack features effectively. Then, the feature pyramid joins together low-resolution, semantically strong features at a high-resolution for obtaining rich semantics. Finally, YOLOv3 is used for the bounding box regression. YOLOv3-Lite is a fast and accurate crack detection method, which can be used on aircraft structure such as fuselage or engine blades. The result shows that, with almost no loss of detection accuracy, the speed of YOLOv3-Lite is 50% more than that of YOLOv3. It can be concluded that YOLOv3-Lite can reach state-of-the-art performance.

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

Feature weighting network for aircraft engine defect detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2050012
Author(s):  
Liqiong Chen ◽  
Lian Zou ◽  
Cien Fan ◽  
Yifeng Liu
Keyword(s):  
Defect Detection ◽  
State Of The Art ◽  
Aircraft Engine ◽  
Air Transportation ◽  
Feature Weighting ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Feature Pyramid ◽  
New Feature

Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications.

scholarly journals An Improved Jitter Detection Method Based on Parallax Observation of Multispectral Sensors for Gaofen-1 02/03/04 Satellites

2018 ◽  
Vol 11 (1) ◽  
pp. 16 ◽  
Author(s):  
Ying Zhu ◽  
Mi Wang ◽  
Yufeng Cheng ◽  
Luxiao He ◽  
Lin Xue
Keyword(s):  
High Resolution ◽  
Multispectral Imaging ◽  
Detection Method ◽  
Negative Influence ◽  
Detection Accuracy ◽  
Comparison Results ◽  
Geometric Quality ◽  
Along Track ◽  
Internal Error ◽  
Multispectral Sensors

Gaofen-1 02/03/04 satellites, the first civilian high resolution optical operational constellation in China, have Earth observation capabilities with panchromatic/multispectral imaging at 2/8 m resolution. Satellite jitter, the fluctuation of satellite points, has a negative influence on the geometric quality of high-resolution optical satellite imagery. This paper presents an improved jitter detection method based on parallax observation of multispectral sensors for Gaofen-1 02/03/04 satellites, which can eliminate the effect of the relative internal error induced by lens distortion, and accurately estimate the parameters of satellite jitter. The relative internal error is estimated by polynomial modelling and removed from the original parallax image generated by pixel-to-pixel image matching between two bands of images. The accurate relative time-varying error and absolute distortion caused by satellite jitter could be estimated by using the sine function. Three datasets of multispectral images captured by Gaofen-1 02/03/04 satellites were used to conduct the experiments. The results show that the relative system errors in both the across- and along-track directions can be modelled with a quadratic polynomial, and satellite jitter with a frequency of 1.1–1.2 Hz in the across-track direction was detected for the first time. The amplitude of the jitter differed in the three datasets. The largest amplitude, from satellite 04, is 1.3 pixels. The smallest amplitude, from satellite 02, is 0.077 pixels. The reliability and accuracy of the detection results were verified by using two groups of band combinations and ortho-images with a 1 m resolution. The comparison results show that the detection accuracy is improved by approximately 30% using the proposed method.

Research on Bolt Crack Detection Method Based on Image Processing

2013 ◽  
Vol 433-435 ◽  
pp. 426-429
Author(s):  
Jin Qiu Liu ◽  
Bing Fa Zhang ◽  
Yu Zeng Wang ◽  
Guang Ya Li ◽  
Jing Ru Han
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Recognition ◽  
Crack Detection ◽  
Detection Method ◽  
Image Acquisition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Contact Detection ◽  
Fracture Identification

A method of non-contact detection of bolt fracture have serial steps as follows: First of all the required data is obtained through image acquisition, then through the edge detection, image recognition and other image processing on the image to get the bolt fracture identification results, finally the non-contact measurement bolt fracture is realized. Experiments show that bolt crack detection method based on image processing, compared with the traditional detection methods improve the efficiency of detection and improve the detection accuracy. The method for bolt crack detection is feasible.

scholarly journals Immature Apple Detection Method Based on Improved Yolov3

2021 ◽  
Vol 1 (1) ◽  
pp. 9-13
Author(s):  
Zhongqiang Huang ◽  
Ping Zhang ◽  
Ruigang Liu ◽  
Dongxu Li
Keyword(s):  
Decision Making ◽  
Feasible Solution ◽  
Detection Method ◽  
Detection Accuracy ◽  
Data Set ◽  
Apple Industry ◽  
Backbone Network ◽  
Target Frame ◽  
Detection Speed ◽  
Detection Effect

The identification of immature apples is a key technical link to realize automatic real-time monitoring of orchards, expert decision-making, and realization of orchard output prediction. In the orchard scene, the reflection caused by light and the color of immature apples are highly similar to the leaves, especially the obscuration and overlap of fruits by leaves and branches, which brings great challenges to the detection of immature apples. This paper proposes an improved YOLOv3 detection method for immature apples in the orchard scene. Use CSPDarknet53 as the backbone network of the model, introduce the CIOU target frame regression mechanism, and combine with the Mosaic algorithm to improve the detection accuracy. For the data set with severely occluded fruits, the F1 and mAP of the immature apple recognition model proposed in this article are 0.652 and 0.675, respectively. The inference speed for a single 416×416 picture is 12 ms, the detection speed can reach 83 frames/s on 1080ti, and the inference speed is 8.6 ms. Therefore, for the severely occluded immature apple data set, the method proposed in this article has a significant detection effect, and provides a feasible solution for the automation and mechanization of the apple industry.

scholarly journals A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (19) ◽  
pp. 3814
Author(s):  
Fang Fang ◽  
Kaishun Wu ◽  
Yuanyuan Liu ◽  
Shengwen Li ◽  
Bo Wan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
State Of The Art ◽  
Complex Structures ◽  
Building Extraction ◽  
Remote Sensing Imagery ◽  
Feature Pyramid ◽  
Coarse To Fine ◽  
Original Feature

Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods’ accuracy and quality of building contours.

scholarly journals Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images

2021 ◽  
Vol 11 (16) ◽  
pp. 7610
Author(s):  
Khurram Azeem Hashmi ◽  
Alain Pagani ◽  
Marcus Liwicki ◽  
Didier Stricker ◽  
Muhammad Zeshan Afzal
Keyword(s):  
Computer Vision ◽  
Relative Error ◽  
State Of The Art ◽  
Error Reduction ◽  
Detection Accuracy ◽  
Document Images ◽  
Backbone Network ◽  
Mathematical Formulas ◽  
Composite Connections ◽  
Areas Of Interest

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

scholarly journals Jitter Detection Method Based on Sequence CMOS Images Captured by Rolling Shutter Mode for High-Resolution Remote Sensing Satellite

2022 ◽  
Vol 14 (2) ◽  
pp. 342
Author(s):  
Ying Zhu ◽  
Tingting Yang ◽  
Mi Wang ◽  
Hanyu Hong ◽  
Yaozong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Detection Method ◽  
Remote Sensing Data ◽  
Error Modeling ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Satellite Platform ◽  
Dense Matching ◽  
Remote Sensing Satellite

Satellite platform jitter is a non-negligible factor that affects the image quality of optical cameras. Considering the limitations of traditional platform jitter detection methods that are based on attitude sensors and remote sensing images, this paper proposed a jitter detection method using sequence CMOS images captured by rolling shutter for high-resolution remote sensing satellite. Through the three main steps of dense matching, relative jitter error analysis, and absolute jitter error modeling using sequence CMOS images, the periodic jitter error on the imaging focal plane of the spaceborne camera was able to be measured accurately. The experiments using three datasets with different jitter frequencies simulated from real remote sensing data were conducted. The experimental results showed that the jitter detection method using sequence CMOS images proposed in this paper can accurately recover the frequency, amplitude, and initial phase information of satellite jitter at 100 Hz, 10 Hz, and 2 Hz. Additionally, the detection accuracy reached 0.02 pixels, which can provide a reliable data basis for remote sensing image jitter error compensation.

scholarly journals ShipYOLO: An Enhanced Model for Ship Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xu Han ◽  
Lining Zhao ◽  
Yue Ning ◽  
Jingfeng Hu
Keyword(s):  
Feature Extraction ◽  
Target Space ◽  
Small Scale ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Ship Detection ◽  
Backbone Network ◽  
Input Size ◽  
Pyramid Structure ◽  
Feature Pyramid

The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3  ×  3 convolution, 1  ×  1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320  ×  320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320  ×  320, ShipYOLO, compared to YOLO-V4.

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