Railway fastener image recognition method based on multi feature fusion

Xu, Ruxun; Han, Jinyue; Qi, Wenzhe; Meng, Jianjun; Zhang, Hongqiang

doi:10.1088/1757-899x/397/1/012119

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…For detecting obstacles on the track, utilising ML technology in comparing input and reference data to train frontal view camera pictures was proposed, therein yielding accurate and successful results in experiments [16]. Moreover, to improve the detection of defects in railway fasteners for improving accuracy and overall safety, ML has been applied to image recognition on railway tracks [3], [17]. Furthermore, to classify wheel failures, a logistic regression model has been developed to predict the possibility of events of high wheel effect train stops, where the results also showed high accuracy [18].…”

Section: Related Work a Railway Applications And Machine Learningmentioning

confidence: 99%

“…The main concern for condition monitoring is the translation of data into information and subsequent employment of that information to improve processes. Machine learning (ML) is a technique for discovering information with self-learning techniques [3], and it has been used in every field due to its ability to obtain useful information from large sets of data [4]. The sector responsible for the railways in the UK, for example, has strategies for digitalising the industry.…”

Section: Introductionmentioning

confidence: 99%

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Learning From Accidents: Machine Learning for Safety at Railway Stations

Alawad

Kaewunruen

2020

IEEE Access

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Section: Related Work a Railway Applications And Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning From Accidents: Machine Learning for Safety at Railway Stations

Alawad

Kaewunruen

2020

IEEE Access

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“…However, local features suffer from the inability to extract features accurately for targets with smooth edges and sensitivity to directional information. Xu et al [13] fused MB-LBP (multiblock local binary pattern) features and PHOG (pyramid histogram of oriented gradients) features to form a new image feature for training and introduced an Adaboost-SVM (support vector machine) classifier to classify the samples. The experimental results showed that this fused image feature method effectively improved the accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

Rail Fastener Status Detection Based on MobileNet-YOLOv4

Chen

2022

Electronics

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As an important part of track inspection, the detection of rail fasteners is of great significance to improve the safety of train operation. Additionally, rail fastener detection belongs to small-target detection. The YOLOv4 algorithm is relatively fast in detection and has some advantages in small-target detection. Therefore, YOLOv4 is used for rail fastener status detection. However, YOLOv4 still suffers from the following two problems in rail fastener status detection. First, the features extracted by the original feature extraction network of YOLOv4 are relatively rough, which is not conducive to crack anomaly detection on rail fasteners. In addition, the traditional convolutional neural network has a larger number of parameters and calculations, which are difficult to run on the embedded system with low memory and processing power. To effectively solve those two problems, this paper proposes a rail fastener status detection algorithm based on MobileNet-YOLOv4 (M-YOLOv4). The edge features and texture features of rail fasteners are very important for rail fastener detection, and CSPDarknet53 cannot effectively extract the features of fasteners. The MobileNet is used to replace the CSPDarknet53 feature extraction network in the YOLOv4 algorithm, which can extract subtle features of rail fasteners and reduce the number of parameters and calculations of the algorithm. The experimental results show that the M-YOLOv4 algorithm has high detection accuracy and low resource consumption in rail fastener status detection. The false-alarm rate (FAR), missed-alarm rate (MAR), and error rate (ER) were 5.71%, 1.67%, and 4.24%, respectively, and the detection speed reached 59.8 fps. Compared with YOLOv4, the number of parameters and calculations were reduced by about 80.75% and 83.20%, respectively.

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“…To study various linguistic phenomena and laws in natural language, it is particularly necessary to describe language knowledge correctly. The study of natural language is not only important for automatic translation (written, voice, and computer) between natural languages but also for information processing related to the understanding of natural language knowledge levels: automatic summarization, text shaping, automatic formation of practical office letters, information filtering, information selection, network integration services, and other fields are also very important [ 9 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition Method of Machine English Translation Errors Based on Multisignal Feature Fusion

Zhang

Huang

2022

Computational Intelligence and Neuroscience

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The current automatic recognition method of machine English translation errors has poor semantic analysis ability, resulting in low accuracy of recognition results. Therefore, this paper designs an automatic recognition method for machine English translation errors based on multifeature fusion. Manually classify and summarize the real error sentence pairs, falsify a large amount of data by means of data enhancement, enhance the effect and robustness of the machine translation error detection model, and add the source text to translation length ratio information and the translation language model PPL into the model input. The score feature information can further improve the classification accuracy of the error detection model. Based on this error detection scheme, the detection results can be used for subsequent error correction and can also be used for error prompts to provide translation user experience; it can also be used for evaluation indicators of machine translation effects. The experimental results show that the word posterior probability features calculated by different methods have a significant impact on the classification error rate, and adding source word features based on the combination of word posterior probability and linguistic features can significantly reduce the classification error rate, to improve the translation error detection ability.

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Railway fastener image recognition method based on multi feature fusion

Cited by 5 publications

References 6 publications

Learning From Accidents: Machine Learning for Safety at Railway Stations

Learning From Accidents: Machine Learning for Safety at Railway Stations

Rail Fastener Status Detection Based on MobileNet-YOLOv4

Automatic Recognition Method of Machine English Translation Errors Based on Multisignal Feature Fusion

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