Road damage detection with bounding box and generative adversarial networks based augmentation methods

Aghayan‐Mashhady, Nima; Amirkhani, Abdollah

doi:10.1049/ipr2.12940

Cited by 2 publications

(1 citation statement)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the experimental results may lack diverse test data and do not fully demonstrate the performance of the model in different scenarios. Aghayan-Mashhady and Amirkhani (2024) developed an algorithm for detecting road damage based on YOLOv5 with several different baseline models. The algorithm utilizes traditional bounding box enhancement and road damage generation adversarial network based enhancement techniques.…”

Section: Introductionmentioning

confidence: 99%

EMG-YOLO: road crack detection algorithm for edge computing devices

Xing,

Han,

Pan

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

IntroductionRoad cracks significantly shorten the service life of roads. Manual detection methods are inefficient and costly. The YOLOv5 model has made some progress in road crack detection. However, issues arise when deployed on edge computing devices. The main problem is that edge computing devices are directly connected to sensors. This results in the collection of noisy, poor-quality data. This problem adds computational burden to the model, potentially impacting its accuracy. To address these issues, this paper proposes a novel road crack detection algorithm named EMG-YOLO.MethodsFirst, an Efficient Decoupled Header is introduced in YOLOv5 to optimize the head structure. This approach separates the classification task from the localization task. Each task can then focus on learning its most relevant features. This significantly reduces the model’s computational resources and time. It also achieves faster convergence rates. Second, the IOU loss function in the model is upgraded to the MPDIOU loss function. This function works by minimizing the top-left and bottom-right point distances between the predicted bounding box and the actual labeled bounding box. The MPDIOU loss function addresses the complex computation and high computational burden of the current YOLOv5 model. Finally, the GCC3 module replaces the traditional convolution. It performs global context modeling with the input feature map to obtain global context information. This enhances the model’s detection capabilities on edge computing devices.ResultsExperimental results show that the improved model has better performance in all parameter indicators compared to current mainstream algorithms. The EMG-YOLO model improves the accuracy of the YOLOv5 model by 2.7%. The mAP (0.5) and mAP (0.9) are improved by 2.9% and 0.9%, respectively. The new algorithm also outperforms the YOLOv5 model in complex environments on edge computing devices.DiscussionThe EMG-YOLO algorithm proposed in this paper effectively addresses the issues of poor data quality and high computational burden on edge computing devices. This is achieved through optimizing the model head structure, upgrading the loss function, and introducing global context modeling. Experimental results demonstrate significant improvements in both accuracy and efficiency, especially in complex environments. Future research can further optimize this algorithm and explore more lightweight and efficient object detection models for edge computing devices.

show abstract

Section: Introductionmentioning

confidence: 99%

EMG-YOLO: road crack detection algorithm for edge computing devices

Xing,

Han,

Pan

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

Pothole detection‐you only look once: Deformable convolution based road pothole detection

Tang,

Lv,

Ding

et al. 2024

IET Image Processing

View full text Add to dashboard Cite

The detection of road potholes plays a crucial role in ensuring passenger comfort and the structural safety of vehicles. To address the challenges of pothole detection in complex road environments, this paper proposes a model focusing on shape features (pothole detection you only look once, PD‐YOLO). The model aims to overcome the limitations of multi‐scale feature learning caused by the use of fixed convolutional kernels in the baseline model, by constructing a feature extraction module that better adapts to variations in the shape of potholes. Subsequently, a cross‐stage partial network was designed using a one‐time aggregation method, simplifying the model while enabling the network to fuse information between feature maps at different stages. Additionally, a dynamic sparse attention mechanism is introduced to select relevant features, reducing redundancy and suppressing background noise. Experiments conducted on the VOC2007 and GRDDC2020_Pothole datasets reveal that compared to the baseline model YOLOv8, PD‐YOLO achieves improvements of 3.9% and 2.8% in mean average precision, with a frame rate of approximately 290 frames per second, effectively meeting the accuracy and real‐time requirements for pothole detection. The code and dataset for this paper are located at: https://github.com/woyijiankou/PD‐YOLO.

show abstract

Road damage detection with bounding box and generative adversarial networks based augmentation methods

Cited by 2 publications

References 72 publications

EMG-YOLO: road crack detection algorithm for edge computing devices

EMG-YOLO: road crack detection algorithm for edge computing devices

Pothole detection‐you only look once: Deformable convolution based road pothole detection

Contact Info

Product

Resources

About