YOLO-Crater Model for Small Crater Detection

Mu, Lingli; Xian, Lina; Li, Lihong; Liu, Gang; Chen, Mi; Zhang, Wei

doi:10.3390/rs15205040

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…The mean average precision (mAP) represents the average of precision computed at various recall levels, serving as a comprehensive metric for model evaluation. The F1, the harmonic mean of precision and recall, is employed to holistically assess the model's accuracy and stability, providing a balanced view of the model performance [55,56].…”

Section: Experimental Environment and Assessment Indicatorsmentioning

confidence: 99%

Optimizing Geo-Hazard Response: LBE-YOLO’s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation

Du,

Xu,

2024

Remote Sensing

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Prompt detection of landslides is crucial for reducing the disaster risk and preventing landslides. However, landslide detection in practical applications still faces many challenges, such as the complexity of environmental backgrounds, the diversity of target scales, and the enormity of model weights. To address these issues, this paper proposes a lightweight LBE-YOLO model for real-time landslide detection. Firstly, a lightweight model is designed by integrating the GhostConv lightweight network with the YOLOv8n model. Inspired by GhostConv, this study innovatively designed the GhostC2f structure, which leverages linear thinking to further reduce the model parameters and computational burden. Additionally, the newly designed EGC2f structure, incorporating an attention mechanism, not only maintains the model’s lightweight characteristics but also enhances the network’s capability to extract valid information. Subsequently, the Path Aggregation Network (PAN) was optimized by introducing a bidirectional feature propagation mechanism to improve the model’s feature fusion ability. Additionally, the Bijie landslide dataset was expanded through data augmentation strategies, thereby further improving the model’s generalization capability. The experimental results indicate that, compared to the YOLOv8n model, the proposed model increased accuracy by 4.2%, while the model’s weight and computational load were reduced by 32.0% and 35.5%, respectively. This verifies the superiority of the LBE-YOLO model in landslide target detection, which will help mitigate the impacts of natural disasters.

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Section: Experimental Environment and Assessment Indicatorsmentioning

confidence: 99%

Optimizing Geo-Hazard Response: LBE-YOLO’s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation

Du,

Xu,

2024

Remote Sensing

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A review on deep learning-based automated lunar crater detection

Chaini,

Jha

2024

Earth Sci Inform

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GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

Zheng,

Liu,

et al. 2024

Sci Rep

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Detecting foreign objects in power transmission lines is essential for mitigating safety risks and maintaining line stability. Practical detection, however, presents challenges including varied target sizes, intricate backgrounds, and large model weights. To address these issues, this study introduces an innovative GEB-YOLO model, which balances detection performance and quantification. Firstly, the algorithm features a lightweight architecture, achieved by merging the GhostConv network with the advanced YOLOv8 model. This integration considerably lowers computational demands and parameters through streamlined linear operations. Secondly, this paper proposes a novel EC2f mechanism, a groundbreaking feature that bolsters the model’s information extraction capabilities. It enhances the relationship between weights and channels via one-dimensional convolution. Lastly, the BiFPN mechanism is employed to improve the model’s processing efficiency for targets of different sizes, utilizing bidirectional connections and swift feature fusion for normalization. Experimental results indicate the model’s superiority over existing models in precision and mAP, showing improvements of 3.7 and 6.8%, respectively. Crucially, the model’s parameters and FLOPs have been reduced by 10.0 and 7.4%, leading to a model that is both lighter and more efficient. These advancements offer invaluable insights for applying laser technology in detecting foreign objects, contributing significantly to both theory and practice.

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YOLO-Crater Model for Small Crater Detection

Cited by 5 publications

References 43 publications

Optimizing Geo-Hazard Response: LBE-YOLO’s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation

Optimizing Geo-Hazard Response: LBE-YOLO’s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation

A review on deep learning-based automated lunar crater detection

GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines

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