Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks

Liu, Zhen; Gu, Xingyu; Chen, Jiaqi; Wang, Danyu; Chen, Yihan; Wang, Lutai

doi:10.1016/j.autcon.2022.104698

Cited by 101 publications

(53 citation statements)

References 41 publications

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“…Detection results showed a precision of 97.68% and recall of 96.88%. Liu et al [ 35 ] used the YOLOv3 model with four-scale detection layers (FDL) to detect combined B-scan and C-scan GPR images. The proposed method can detect both large particles and small cracks.…”

Section: Related Workmentioning

confidence: 99%

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Nguyen¹,

Nguyen²,

Scherer

et al. 2023

Sensors

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Hand detection and classification is a very important pre-processing step in building applications based on three-dimensional (3D) hand pose estimation and hand activity recognition. To automatically limit the hand data area on egocentric vision (EV) datasets, especially to see the development and performance of the “You Only Live Once” (YOLO) network over the past seven years, we propose a study comparing the efficiency of hand detection and classification based on the YOLO-family networks. This study is based on the following problems: (1) systematizing all architectures, advantages, and disadvantages of YOLO-family networks from version (v)1 to v7; (2) preparing ground-truth data for pre-trained models and evaluation models of hand detection and classification on EV datasets (FPHAB, HOI4D, RehabHand); (3) fine-tuning the hand detection and classification model based on the YOLO-family networks, hand detection, and classification evaluation on the EV datasets. Hand detection and classification results on the YOLOv7 network and its variations were the best across all three datasets. The results of the YOLOv7-w6 network are as follows: FPHAB is P = 97% with TheshIOU = 0.5; HOI4D is P = 95% with TheshIOU = 0.5; RehabHand is larger than 95% with TheshIOU = 0.5; the processing speed of YOLOv7-w6 is 60 fps with a resolution of 1280 × 1280 pixels and that of YOLOv7 is 133 fps with a resolution of 640 × 640 pixels.

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Section: Related Workmentioning

confidence: 99%

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Nguyen¹,

Nguyen²,

Scherer

et al. 2023

Sensors

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“…In fact, if a feature extractor with better performance appears in the future, the experimental effect may be better, but the idea proposed in this paper is still applicable at that time. Therefore, our next step is to improve and integrate the feature extraction model with a stronger multiscale feature extraction capability and less computational complexity with the semantic segmentation model used in this paper, such as the new modified YOLOv3 model with four scale detection layers [38], which shows an excellent multiscale feature extraction performance. Furthermore, in future experiments, we will further extend the model experiments to explore multi-spectral remote sensing data.…”

Section: Improvements and Future Workmentioning

confidence: 99%

“…In particular, deep convolutional neural networks (DCNNs) with strong feature extraction capabilities are popular in computer vision [30][31][32][33][34][35][36][37]. For instance, Reference [38] designed a YOLOv3 model with four scale detection layers for pavement crack detection, using a multiscale fusion structure and an efficient cross-linking (EIoU) loss function. Recently, DCNNs have been gradually used in image semantic segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF

Cheng

Liu

2023

Remote Sensing

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The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing images have brought great challenges to remote sensing image segmentation tasks. Traditional convolutional-neural-network-based models often ignore spatial information in the feature extraction stage and pay less attention to global context information. However, spatial context information is important in complex remote sensing images, which means that the segmentation effect of traditional models needs to be improved. In addition, neural networks with a superior segmentation performance often suffer from the problem of high computational resource consumption. To address the above issues, this paper proposes a combination model of a modified multiscale deformable convolutional neural network (mmsDCNN) and dense conditional random field (DenseCRF). Firstly, we designed a lightweight multiscale deformable convolutional network (mmsDCNN) with a large receptive field to generate a preliminary prediction probability map at each pixel. The output of the mmsDCNN model is a coarse segmentation result map, which has the same size as the input image. In addition, the preliminary segmentation result map contains rich multiscale features. Then, the multi-level DenseCRF model based on the superpixel level and the pixel level is proposed, which can make full use of the context information of the image at different levels and further optimize the rough segmentation result of mmsDCNN. To be specific, we converted the pixel-level preliminary probability map into a superpixel-level predicted probability map according to the simple linear iterative clustering (SILC) algorithm and defined the potential function of the DenseCRF model based on this. Furthermore, we added the pixel-level potential function constraint term to the superpixel-based Gaussian potential function to obtain a combined Gaussian potential function, which enabled our model to consider the features of various scales and prevent poor superpixel segmentation results from affecting the final result. To restore the contour of the object more clearly, we utilized the Sketch token edge detection algorithm to extract the edge contour features of the image and fused them into the potential function of the DenseCRF model. Finally, extensive experiments on the Potsdam and Vaihingen datasets demonstrated that the proposed model exhibited significant advantages compared to the current state-of-the-art models.

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“…Neural network recognition methods are often used for processing visual data and are widely used in the field of vehicular traffic. A neural network based on multi-scale feature fusion was proposed in Reference [ 31 ]. This neural network is trained to combine the different features of two scanned images and thus identify the cracks in the pavement.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion

Zou

Zheng

Wang

2023

Sensors

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To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes.

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Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks

Cited by 101 publications

References 41 publications

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion

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