A Study on Railway Surface Defects Detection Based on Machine Vision

Bai, Tangbo; Gao, Jialin; Yang, Jianwei; Yao, Dechen

doi:10.3390/e23111437

Cited by 24 publications

(7 citation statements)

References 26 publications

(25 reference statements)

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“…However, owing to the limited and imbalanced nature of the dataset employed by the system, the model encounters a notable overfitting phenomenon, thereby resulting in potential omissions and recognition errors during realworld testing scenarios. Tangbo Bai et al [19] proposed an enhanced YOLOv4-based method for detecting defects on railroad surfaces. The proposed approach employs MobileNetv3 as the underlying architecture for the extraction of image features within the YOLOv4 framework.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv8 Algorithm for Rail Surface Defect Detection

Wang,

Zhang,

Wang

et al. 2024

IEEE Access

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To tackle the issues raised by detecting small targets and densely occluded targets in railroad track surface defect detection, we present an algorithm for detecting defects on railroad tracks based on the YOLOv8 model. Firstly, we enhance the model's attention towards small and medium-sized targets by substituting replacing the original convolution with the SPD-Conv building block in the backbone network of YOLOv8n, while preserving the original network structure. Secondly, we integrate the integrating the EMA attention mechanism module into the neck component, allowing the model to leverage information from different layers of features and improve feature representation capabilities. Lastly, we substitute the original C-IOU with the Focal-SIoU loss function in YOLOv8., which adjusts the weights of positive and negative samples to penalize difficult-to-classify samples more heavily. This enhancement improves the model's capability to accurately recognize challenging samples and ensures that the network allocates greater attention to each target instance, resulting in improved performance and effectiveness of the model. The experimental results reveal notable advancements in precision, recall, and average accuracy attained by our enhanced algorithm. Compared to the original YOLOv8n model, our enhanced algorithm demonstrates remarkable precision, recall, and average accuracy of 93.9%, 93.7%, and 94.1%, respectively. These improvements amount to 3.6%, 5.0%, and 5.7%, respectively. Notably, these enhancements are accomplished while maintaining the dimensions of the model and the parameter count. During the identification of defects on railroad track surfaces, our improved algorithm surpasses other widely used algorithms in terms of performance.

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Section: Introductionmentioning

confidence: 99%

An Improved YOLOv8 Algorithm for Rail Surface Defect Detection

Wang,

Zhang,

Wang

et al. 2024

IEEE Access

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“…At the same time, with the booming development of computer intelligent technology, domestic and foreign scholars have done a lot of research in extracting power lines [3], insulation fault and defect detection [4][5], and transmission tower and sign recognition [6][7], and have proposed various solutions for foreign body detection, achieving relatively ideal results. Compared with power line ex-traction and insulation fault detection, research on foreign body detection on transmission lines is relatively scarce.…”

Section: Introductionmentioning

confidence: 99%

A foreign body intrusion detection method for power lines based on Transformer

Huang,

Wu,

Cai

et al. 2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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The power system is constantly exposed to outdoor environments, which makes it susceptible to invasion by foreign body such as tree branches and garbage bags. Currently, most deep learning-based detection methods assume the presence of foreign body in the image, and there is still room for improvement in detection accuracy. In this paper, a foreign body detection method for the power system is proposed based on Inception-V3 and Trans-former. The method first classifies inspection images according to whether foreign bodies are present, and then detects foreign body that have invaded the power system. This method does not use pre-defined datasets and converts object detection into a direct bounding box prediction problem, which greatly optimizes existing detection methods. Experimental results on actual datasets show that our research effectively improves the accuracy and efficiency of foreign body detection compared to detection algorithms based on Faster R-CNN and YOLOv3.

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“…It is a cross domain of computer vision, digital image processing, and machine vision. It always had high research and application value in face recognition [ 1 , 2 ], industrial defect detection [ 3 ], UAV aviation detection [ 4 , 5 ], traffic and vehicle detection [ 6 ], pedestrian detection and counting [ 7 ], and other fields.…”

Section: Introductionmentioning

confidence: 99%

SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode

Liu

Sun

Deng

2022

Sensors

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In the research of computer vision, a very challenging problem is the detection of small objects. The existing detection algorithms often focus on detecting full-scale objects, without making proprietary optimization for detecting small-size objects. For small objects dense scenes, not only the accuracy is low, but also there is a certain waste of computing resources. An improved detection algorithm was proposed for small objects based on YOLOv5. By reasonably clipping the feature map output of the large object detection layer, the computing resources required by the model were significantly reduced and the model becomes more lightweight. An improved feature fusion method (PB-FPN) for small object detection based on PANet and BiFPN was proposed, which effectively increased the detection ability for small object of the algorithm. By introducing the spatial pyramid pooling (SPP) in the backbone network into the feature fusion network and connecting with the model prediction head, the performance of the algorithm was effectively enhanced. The experiments demonstrated that the improved algorithm has very good results in detection accuracy and real-time ability. Compared with the classical YOLOv5, the mAP@0.5 and mAP@0.5:0.95 of SF-YOLOv5 were increased by 1.6% and 0.8%, respectively, the number of parameters of the network were reduced by 68.2%, computational resources (FLOPs) were reduced by 12.7%, and the inferring time of the mode was reduced by 6.9%.

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A Study on Railway Surface Defects Detection Based on Machine Vision

Cited by 24 publications

References 26 publications

An Improved YOLOv8 Algorithm for Rail Surface Defect Detection

An Improved YOLOv8 Algorithm for Rail Surface Defect Detection

A foreign body intrusion detection method for power lines based on Transformer

SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode

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