Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet

Shafi, Imran; Mazahir, Awais; Fatima, Anum; Ashraf, Imran

doi:10.1016/j.measurement.2022.111836

Cited by 25 publications

(5 citation statements)

References 18 publications

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“…In this experiment, we compare deep learning algorithms and some state-of-theart classification methods with MFAF to demonstrate that it outperforms other models in prospecting target prediction tasks. Specifically, we compare the following methods, including the deep learning methods ResNet 18 [48], ShuffleNetV2 [49], GoogLeNet [50], MobileNetV2 [51] and MnasNet [52]. In a deep learning algorithm, we compress each data point in the geochemical dataset into a one-dimensional tensor as the input to the algorithm.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Gao,

Wang,

Zhang

et al. 2023

Sustainability

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In recent years, the intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the main research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example: (1) There are very few trainable image samples containing mineral point labels; (2) the geological image features are small and irregular, and the image similarity is high; (3) it is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model multiscale feature attention framework (MFAF) based on geoimage data. The results show that the MFCA-Net module in the MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference in influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of the Qin-hang metallogenic belt. The experimental results show that the areas numbered 5, 9, 16, 28, 34, 41, 50, 72, 74, 75, 80, 97, 101, 124, and 130 have great metallogenic potential and this method would be a promising tool for metallogenic prediction. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved. The multi-scale feature fusion and attention mechanism MFAF in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantees and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.

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Section: Experiments Results and Analysismentioning

confidence: 99%

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Gao,

Wang,

Zhang

et al. 2023

Sustainability

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“…MobileNetV2 model is also proposed in this study because it is an efficient convolutional neural network for modern object detection systems with trainable parameters and can detect objects properly. MobileNet architecture is already tiny and has low latency but is rich in features, so it supports applications that require models to be smaller and faster [20][21][22]26]. Besides being fast and small, the MobileNetV2 structure is built to detect objects with training time faster (as shown in Table 2).…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…This research trains these newly added layers on the selected datasets so that the model can fix the features to detect yawning drivers. The use of the MobileNetV2 architecture is because this model has been developed a lot in the implementation stage of localization (position) of object detection using a device, as has been done in previous research on palmprint recognition and facemask position recognition [20][21][22]. ResNet50 is also more widely used for image classification than other models and has shown great results in computer vision applications, such as high-resolution optical object detection [23][24][25].…”

Section: Mobilenetv2 and Resnet50mentioning

confidence: 99%

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Ilmadina,

Naufal,

Wibowo

2023

matrik

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Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results.

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“…In the downstream application combined with the lightweight model, Fu et al [39] proposed an improved YOLOv5 algorithm based on MobileNetv3 and introduced coordinate attention attention to improve the detection accuracy of complex surface defects on bearings. Shafi et al [40] proposed a hollow cylinder surface defect detection method, which uses a pipeline mirror to capture images and locates and classifies defects using SSD and MobileNet respectively. The above algorithms all use lightweight modules and network structures as feature extraction networks, which bring certain lightweight effects.…”

Section: Faster and Lighter Modelsmentioning

confidence: 99%

FLCNet: faster and lighter cross-scale feature aggregation network for lead bar surface defect detection

Lv,

Xia,

et al. 2024

Meas. Sci. Technol.

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Aiming at the defect inspection under the characteristics of scale change, high reflection, inclined deformation of defects of lead bars and meeting the needs for faster detection, this paper proposes a faster and lighter cross-scale feature aggregation network (FLCNet). In this study, we focus on the redundancy of channel information, and design a new partial channel group convolution, based on which we design a Faster C3 module and a lightweight cross-scale feature fusion module. In addition, we design a cross-scale slim neck to reduce the redundant feature transfer of the model. Finally, we propose a uniform brightness acquisition method for lead bar sidewall image by using combined light source and construct a lead bar dataset with various complex defect samples. Experiments show that FLCNet effectively improves the detection accuracy of the surface defects of lead bars, the mAP@0.5 value reaches 97.1%, and compared with YOLOv5s, the model’s parameters reduced by 33.9%. At the same time, the detection speed reaches 114.9 FPS, which is faster than other advanced detection models.

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Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet

Cited by 25 publications

References 18 publications

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

FLCNet: faster and lighter cross-scale feature aggregation network for lead bar surface defect detection

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