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Wang Niannian

Bio: Wang Niannian is an academic researcher from Zhengzhou University. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has co-authored 5 publications.

Papers
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Journal ArticleDOI
Duo Ma1, Hongyuan Fang1, Wang Niannian1, Binghan Xue1, Dong Jiaxiu1, Fu Wang1 
TL;DR: This work developed a pavement crack detection method based on a convolutional neural network (CNN) with multiple feature detectors that are sensitive to small objects like pavement cracks.
Abstract: Conventional algorithms are not sensitive to small objects like pavement cracks. We developed a pavement crack detection method based on a convolutional neural network (CNN) with multiple feature l...

22 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a multi-defect detection system based on StyleGAN-SDM and fusion CNN for sewer pipelines, which integrates StyleGAN v2 and sharpness discrimination model (SDM) to automatically select clear images.

15 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed a road-mask R-CNN mobile damage detection model to automatically segment and measure multiple pavement damages, where the optimized k-means clustering algorithm is used to intelligently determine the size and ratio of the anchor.
Abstract: Regular damage detection plays an important role in timely pavement maintenance. However, the existing detection methods struggle to efficiently and accurately identify the category and contour of the damage. Therefore, this paper proposes a Road-Mask R-CNN mobile damage detection model to automatically segment and measure multiple pavement damages. First, the optimized k-means clustering algorithm is used to intelligently determine the size and ratio of the anchor. Subsequently, the traditional nonmaximum suppression (NMS) algorithm is replaced by the distance intersection over union nonmaximum suppression (DIoU-NMS) algorithm, which improves the detection accuracy of multiple damages in the same image with a mean average precision (mAP) value of 0.934. Then, a comparative experiment with U-Net, the unimproved Mask R-CNN, MSNet and the unsupervised domain adaptation network (UDA) is carried out to verify the effectiveness of the proposed model. And combined with the segmentation and measurement results, the damage is quantitatively evaluated. Moreover, a webcam damage detection system combined with a workstation and an automatic damage detection system for smartphones is developed to quickly detect multiple types of pavement damage. In addition, on- site experiments are carried out on real pavements to verify the feasibility and effectiveness of the proposed method.

2 citations

Journal ArticleDOI
01 Apr 2021
TL;DR: In this article, Mask R-CNN algorithm is used to segment the target image and measure the pixel size of the target object, which can meet the requirements of airport pavement health inspection.
Abstract: This paper presents a rapid detection and pixel size measurement method for airport pavement apparent disease and Foreign Object Debris. Firstly, MobileNet-SSD algorithm is used for object identification. Then Mask R-CNN algorithm is used to segment the target image and measure the pixel size of the target object. Finally, the detailed information of the target object is obtained. The experimental verification shows that the recognition speed of this method reaches 65 frames per second, and the pixel segmentation accuracy reaches 96 %, which can meet the requirements of airport pavement health inspection.

Cited by
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Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed an efficient and robust sewer defect localization framework motivated by the state-of-the-art detection transformer (DETR) architecture, which views object localization as a set prediction topic.

30 citations

Journal ArticleDOI
01 May 2022-Sensors
TL;DR: It is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.
Abstract: To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.

19 citations

Journal ArticleDOI
TL;DR: A small sample gear face defect detection method based on a Deep convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper which achieves a high score and is better than that of the classic Vgg11 network model.
Abstract: Online defect detection in small industrial parts is of paramount importance for building closed loop intelligent manufacturing systems. However, high-efficiency and high-precision detection of surface defects in these manufacturing systems is a difficult task and poses a major research challenge. The small sample size of industrial parts available for training machine learning algorithms and the low accuracy of computer vision-based inspection algorithms are the bottlenecks that restrict the development of efficient online defect detection technology. To address these issues, we propose a small sample gear face defect detection method based on a Deep Convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper. Initially, we perform data augmentation by using DCGAN and traditional data enhancement methods which effectively increase the size of the training data. In the next stage, we perform defect classification by using a lightweight CNN model which is based on the state-of-the-art Vgg11 network. We introduce the Leaky ReLU activation function and a dropout layer in the proposed CNN. In the experimental evaluation, the proposed framework achieves a high score of 98.40%, which is better than that of the classic Vgg11 network model. The method proposed in this paper is helpful for the detection of defects in industrial parts when the available sample size for training is small.

16 citations

Journal ArticleDOI
01 Nov 2022
TL;DR: Wang et al. as mentioned in this paper proposed an automatic intelligent detection and tracking system for pavement cracks, which is formed of a pavement crack generative adversarial network (PCGAN) and a crack detector and tracking network called YOLO-MF.
Abstract: The regular detection of pavement cracks is critical for life and property security. However, existing deep learning-based methods of crack detection face difficulties in terms of data acquisition and defect counting. An automatic intelligent detection and tracking system for pavement cracks is proposed. Our system is formed of a pavement crack generative adversarial network (PCGAN) and a crack detection and tracking network called YOLO-MF. First, PCGAN is used to generate realistic crack images, to address the problem of the small number of available images. Next, YOLO-MF is developed based on an improved YOLO v3 modified by an acceleration algorithm and median flow (MF) algorithm to count the number of cracks. In a counting loop, our improved YOLO v3 detects cracks and the MF algorithm tracks the cracks detected in a video. This improved algorithm achieves the best accuracy of 98.47% and F1 score of 0.958 among other algorithms, and the precision-recall curve was close to the top right. A tiny model was developed and an acceleration algorithm was applied, which improved the detection speed by factors of five and six, respectively. In on-site measurement, three cracks were detected and tracked, and the total count was correct. Finally, the system was embedded in an intelligent device consisting of a calculating module, an automated unmanned aerial vehicle, and other components.

16 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a novel model named Crack Transformer (CT), which unifies Swin Transformer as the encoder and the decoder with all multi-layer perception (MLP) layers, for the automatic detection of long and complicated pavement cracks.

12 citations