Pavement Disease Detection through Improved YOLOv5s Neural Network

Chu, Yinze; Xiang, Xinjian; Wang, Yilin; Huang, Bingqiang

doi:10.1155/2022/1969511

Cited by 8 publications

(2 citation statements)

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“…The model established by this method is characterized by its high flexibility, fast detection speed, small size, low deployment cost, and strong applicability 48 . It replaces the Conv and Cross‐Stage Partial bottleneck with 3 convolutions (C3) structures in the backbone network with the GhostNet structure, achieving a lightweight YOLOv5 by reducing computational demands and speeding up target detection efficiency 49–51 . To enhance channel information and improve the model's ability to extract target feature information, a lightweight Convolutional Block Attention Module (CBAM) is introduced 52 .…”

Section: Introductionmentioning

confidence: 99%

“…48 It replaces the Conv and Cross-Stage Partial bottleneck with 3 convolutions (C3) structures in the backbone network with the GhostNet structure, achieving a lightweight YOLOv5 by reducing computational demands and speeding up target detection efficiency. [49][50][51] To enhance channel information and improve the model's ability to extract target feature information, a lightweight Convolutional Block Attention Module (CBAM) is introduced. 52 Finally, the Efficient Intersection over Union (EIOU) loss function is used instead of the original Complete Intersection over Union (CIOU) loss function to accelerate convergence and enhance regression precision.…”

Section: Introductionmentioning

confidence: 99%

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Carbonate reservoir fracture‐cavity system identification based on the improved YOLOv5s deep learning algorithm

Feng,

Zhao,

Zhang

et al. 2024

Energy Science & Engineering

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In carbonate reservoirs characterized by the fracture‐cavity system as storage spaces, the drilling process is highly prone to the loss of drilling fluid. This not only affects drilling efficiency but can also lead to severe accidents, such as blowouts. Therefore, it is crucial to understand the distribution pattern of these fractures. However, the formation of carbonate rock fracture‐cavity system systems, being controlled by various factors, is difficult to precisely identify. This limitation hampers the efficient development of such types of oil and gas fields. This paper presents a case study of the M55 sub‐section carbonate gas reservoir in the Sulige gasfield, proposing an improved You Only Look Once v5s (YOLOv5s) deep learning algorithm. It utilizes enhanced training with conventional logging data to identify response characteristics of fractures in the carbonate reservoirs. And its identification results have been confirmed to be accurate by various fracture data obtained through different means, such as the core samples, cast thin section photographs, imaging logging data, and seismic attributes. This method incorporates the Ghost convolution module to replace the Conv module in the backbone network of the YOLOv5s model, and modifies the C3 module into a Ghost Bottleneck module, effectively making the model more lightweight. Additionally, a Convolutional Block Attention Module is integrated into the Neck network, enhancing the model's feature extraction capabilities. Finally, the method employs the Efficient Intersection over Union Loss function instead of the Complete Intersection over Union Loss, reducing the network's regression loss. The validation results using actual data demonstrate that this method achieves an average recognition accuracy of 87.3% for the fracture‐cavity system, which is a 3% improvement over the baseline model (YOLOv5s). This enhancement is beneficial for precisely locating the drilling fluid loss positions in carbonate reservoirs.

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Section: Introductionmentioning

confidence: 99%