A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning

Zhi-jian, Huang; Li, Fangmin; Luan, Xidao; Cai, Zuowei

doi:10.1155/2020/3510313

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Therefore, an active learning method is explained in in the study of Dai et al [18] to reduce the labeling workload. Based on deep learning, a novel supervised method is proposed by Huang et al [19]. This method will adjust the layers in YOLOv3 architecture with more valuable samples.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

Vengaloor¹,

Muralidhar²

2022

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Automatic detection of anomalies on the metal surface is an essential capability in industries to provide the better-quality control. To locate and identify the type of defect, it is necessary to find the Region of interest (RoI) from the captured image. Segmentation of the captured image is one among the many methods to achieve this task. Therefore, a precise and accurate segmentation method has major role to improve the metal surface anomaly detection rate in industry. As the defects are different in it’s size, shape and type, the process of semantic segmentation for metal surface is considered as a challenging task. To address this issue, a deep learning based high calibre U- shaped network is proposed. It can be considered as an automatic quality control system for industries. The proposed method is effective in predicting the presence of defects. The system is also capable to locate the position of the defect on surface without the intervention of human being. The up-sampling technique provided with the convolutional neural network in the architecture makes the system to produce high resolution outputs. The proposed system has been evaluated based on accuracy, precision, loss and IoU after training and testing the model using two different datasets called NEU metal surface defect database and Kolektor surface defect data set.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

Vengaloor¹,

Muralidhar²

2022

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“…As is commonly known, training a deep learning model needs a large amount of exactly labeled samples, which is expensive and time-consuming. Based on this, reference [17] proposed a novel weakly supervised method based on deep active learning. It iteratively adjusted the last few layers of the YOLOv3 model with the most valuable samples, which is selected by a less confident strategy.…”

Section: Introductionmentioning

confidence: 99%

Metal Surface Defect Detection Using Modified YOLO

Zhang

Wang

2021

Algorithms

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Aiming at the problems of inefficient detection caused by traditional manual inspection and unclear features in metal surface defect detection, an improved metal surface defect detection technology based on the You Only Look Once (YOLO) model is presented. The shallow features of the 11th layer in the Darknet-53 are combined with the deep features of the neural network to generate a new scale feature layer using the basis of the network structure of YOLOv3. Its goal is to extract more features of small defects. Furthermore, then, K-Means++ is used to reduce the sensitivity to the initial cluster center when analyzing the size information of the anchor box. The optimal anchor box is selected to make the positioning more accurate. The performance of the modified metal surface defect detection technology is compared with other detection methods on the Tianchi dataset. The results show that the average detection accuracy of the modified YOLO model is 75.1%, which ia higher than that of YOLOv3. Furthermore, it also has a great detection speed advantage, compared with faster region-based convolutional neural network (Faster R-CNN) and other detection algorithms. The improved YOLO model can make the highly accurate location information of the small defect target and has strong real-time performance.

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Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning

Amaral,

Roshan,

Bayat

2023

J. Pipeline Syst. Eng. Pract.

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A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning

Cited by 5 publications

References 22 publications

Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

Metal Surface Defect Detection Using Modified YOLO

Automatic Detection and Classification of Underground Objects in Ground Penetrating Radar Images Using Machine Learning

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