Autonomous surface crack identification of concrete structures based on the YOLOv7 algorithm

Ye, Guanting; Qu, Jinsheng; Tao, Jintai; Dai, Wei; Mao, Yifei; Jin, Qiang

doi:10.1016/j.jobe.2023.106688

Cited by 17 publications

(4 citation statements)

References 17 publications

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“…However, SSD has low detection accuracy for small-sized or occluded objects and lacks the ability to extract feature information from objects. Its detection efficiency is comparable to that of Faster R-CNN [10]. The YOLO network framework design has evolved from v1 to v7, focusing on improving the speed, accuracy, and recall of object prediction.…”

Section: A Object Detectionmentioning

confidence: 99%

BP-YOLO: A Real-Time Product Detection and Shopping Behaviors Recognition Model for Intelligent Unmanned Vending Machine

Li,

Tang,

Zhu

et al. 2024

IEEE Access

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Intelligent unmanned vending machines (UVMs) based on machine vision have attracted great attention in the unmanned retail industry. However, due to the complexity of practical application scenarios and environments, the existing vision-based intelligent UVMs face challenges related to missed-detection and mis-detection of product, and require costly physical components such as the infrared radio frequency sensors to capture shopping behaviors. In this study, we propose a BP-YOLO, the real-time model that integrates optimized YOLOv7 and BlazePose for product detection and shopping behaviors recognition. BP-YOLO can accurately detect the products purchased by consumers and their shopping behaviors in complex scenarios. To address the problems of missed-detection and mis-detection, we introduce the 3D attention mechanism SimAM and the deformable ConvNets v2 (DCNv2) to recombine and optimize the one-stage object detection model YOLOv7. This method reduces the interference of the invalid information in complex scenarios by adaptively weighting each channel and 3D spatial features, focuses on feature information in a sparse space, and minimizes the loss of feature information during the transmission process based on multiscale feature extraction and fusion. To recognize and judge the shopping behaviors of consumers, we track the hand and arm key points of consumers using the pose estimation model BlazePose. Using the mAP@[0.5:0.95] as the evaluation metric for product detection, the experimental results on a customized product dataset show that BP-YOLO achieves an average accuracy of 96.17% for all product categories detection; the average success rate of consumer shopping recognition reaches 92%, 98%, and 94.7% under three light and noise intensity, respectively. Therefore, our BP-YOLO model for intelligent UVMs has effectiveness in commercial deployment.

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Section: A Object Detectionmentioning

confidence: 99%

BP-YOLO: A Real-Time Product Detection and Shopping Behaviors Recognition Model for Intelligent Unmanned Vending Machine

Li,

Tang,

Zhu

et al. 2024

IEEE Access

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“…Ye G. et al [ 3 ] proposed an improved YOLOv7 network, designing and enhancing multiple different self-developed modules to better identify many misleading targets of concrete cracks. Chen J. et al [ 4 ] introduced small object detection layer, lightweight convolution and CBAM (Convolutional Block Attention Module) attention mechanism to achieve multiscale feature extraction and fusion, reducing the number of model parameters.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7

Huang

Wang

Chen

et al. 2023

Sensors

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Existing pavement defect detection models face challenges in balancing detection accuracy and speed while being constrained by large parameter sizes, hindering deployment on edge terminal devices with limited computing resources. To address these issues, this paper proposes a lightweight pavement defect detection model based on an improved YOLOv7 architecture. The model introduces four key enhancements: first, the incorporation of the SPPCSPC_Group grouped space pyramid pooling module to reduce the parameter load and computational complexity; second, the utilization of the K-means clustering algorithm for generating anchors, accelerating model convergence; third, the integration of the Ghost Conv module, enhancing feature extraction while minimizing the parameters and calculations; fourth, introduction of the CBAM convolution module to enrich the semantic information in the last layer of the backbone network. The experimental results demonstrate that the improved model achieved an average accuracy of 91%, and the accuracy in detecting broken plates and repaired models increased by 9% and 8%, respectively, compared to the original model. Moreover, the improved model exhibited reductions of 14.4% and 29.3% in the calculations and parameters, respectively, and a 29.1% decrease in the model size, resulting in an impressive 80 FPS (frames per second). The enhanced YOLOv7 successfully balances parameter reduction and computation while maintaining high accuracy, making it a more suitable choice for pavement defect detection compared with other algorithms.

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“…Most of the research objects of the above models are concerned about concrete cracks and road cracks (Bang et al, 2019; Cui et al, 2021; Li et al, 2021; Ye et al, 2023; Yang et al, 2018), while limited researches had been conducted on the identification of fatigue cracks for steel bridges. Xu et al (2017) proposed a framework for identifying and extracting cracks from images of steel box girders on bridges using a restricted Boltzmann machine algorithm and consumer-grade camera images.…”

Section: Introductionmentioning

confidence: 99%

Automated fatigue crack detection of orthotropic steel decks using an improved mask region-based convolutional neural network approach

Zhao,

Wang,

Cai

et al. 2023

Advances in Structural Engineering

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Orthotropic steel decks (OSD) are widely used in the bridge industry, but they are susceptible to fatigue cracking due to various factors such as weld defects, overload traffic, and complicated welded joint configurations. Traditional crack detection methods rely mainly on visual inspection, which is time-consuming and prone to errors. Recently, many crack detection methods based on machine vision have been introduced. However, accurately and effectively detecting fatigue cracks of OSD remains challenging due to the indistinct nature of these cracks compared to the background and their susceptibility to various types of noise. To address these problems, this research proposes an improved Mask Region-based Convolutional Neural Network (Mask R-CNN). The proposed model enhances the feature extraction ability of the original Mask R-CNN by introducing a Convolutional Block Attention Module (CBAM) and Path Augmentation Feature Pyramid Network (PAFPN) into the backbone network. Additionally, a morphological closing operation is performed on the extracted fatigue cracks to address the problem of disjoint cracks. On the basis of crack mask closure, the pixel-wise quantization of cracks was achieved using relevant OpenCV functions. Evaluation on a crack dataset of OSD shows that the proposed model outperforms other methods with precision, recall, average precision (AP), and F1 score of 83.98%, 84.44%, 77.9%, and 84.21%, respectively. In conclusion, the experiment validates that the proposed model can effectively detect, segment, and quantify fatigue cracks of OSD.

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Autonomous surface crack identification of concrete structures based on the YOLOv7 algorithm

Cited by 17 publications

References 17 publications

BP-YOLO: A Real-Time Product Detection and Shopping Behaviors Recognition Model for Intelligent Unmanned Vending Machine

BP-YOLO: A Real-Time Product Detection and Shopping Behaviors Recognition Model for Intelligent Unmanned Vending Machine

Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7

Automated fatigue crack detection of orthotropic steel decks using an improved mask region-based convolutional neural network approach

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