Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Meng, Lili; Cui, Xi; Liu, Ran; Zheng, Zhi; Shao, Hongli; Liu, Jinxiang; Peng, Yao; Zheng, Lei

doi:10.3390/pr11092564

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…The ShuffleNet_V2 architecture is utilized for producinga set of feature vectors. The major function of the Shufflenet_V2 architecture is the residual block (unit), which comprises a 2 branches [19]. At first,it carries out a channel division at input and splits the input feature maps into a 2subdivisions; the former has3 convolutionalfunctions and the next branch doesn'tcarry outany task, the input and output channel groups of all the branches remain unchanged.…”

Section: Feature Extractionmentioning

confidence: 99%

Reptile Search Algorithm with Deep Convolutional Neural Network for Cloud Assisted Colorectal Cancer Detection and Classification

S. Murugappan

2023

tjjpt

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Cloud-based automatic colorectal cancer (CC) detection involves the usage of cloud computing technology and system to help in the earlier and accurate diagnosis of CC in medical images and patient information. This cloud-based technology aims to improve the efficiency and reliability of CC screening, monitoring, and diagnoses. Automatic CC detection refers to the use of computer-based technology and systems to aid in the earlier and accurate detection of CC in patient data and medical images. This automated system aims to increase the reliability and efficiency of CC monitoring, screening, and diagnosis. Deep learning (DL) methods, especially convolutional neural networks (CNNs), exhibit promising results in automatic CC diagnosis. They can be trained on wide-ranging datasets of medical images to learn patterns and features related to precancerous and cancerous lesion. This study develops a new Reptile Search Algorithm with Deep Learning for Colorectal Cancer Detection and Classification (RSADL-CCDC) technique. The main aim of the RSADL-CCDC method focuses on the automaticclassification and recognition of the CC in the cloud environment. Once the medical images are stored in the cloud server, the detection process is carried out. In the presented RSADL-CCDC approach, the initial stage of preprocessing is performed by bilateral filtering (BF) approach. For feature extraction, the RSADL-CCDC technique applies ShuffleNetv2 model. Besides, the recognition and classification of CC take place using convolutional autoencoder (CAE) model. Finally, the hyperparameter tuning of the CAE technique takes place by utilizing RSA. The experimental validation of the RSADL-CCDC system is performed on benchmark medical database. Extensive results stated the enhanced performance of the RSADL-CCDC technique on CC recognition over other models with respect tovarious actions.

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Section: Feature Extractionmentioning

confidence: 99%

Reptile Search Algorithm with Deep Convolutional Neural Network for Cloud Assisted Colorectal Cancer Detection and Classification

S. Murugappan

2023

tjjpt

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A new network model for multiple object detection for autonomous vehicle detection in mining environment

Hanif,

Yu,

Bashir

et al. 2024

IET Image Processing

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Considering the challenges of low multi‐object detection accuracy and difficulty in identifying small targets caused by challenging environmental conditions including irregular lighting patterns and ambient noise levels in the mining environment with autonomous electric locomotives. A new network model based on SOD−YOLOv5s−4L has been proposed to detect multi‐objects for autonomous electric locomotives in underground coal mines. Improvements have been applied in YOLOv5s to construct the SOD−YOLOv5s−4L model, by introducing the SIoU loss function to address the mismatch between real and predicted bounding box directions, facilitating the model to learn target position information more efficiently. This research introduces a decoupled head to enhance feature fusion and improve the positioning precision of the network model, enabling rapid capture of multi‐scale target features. Furthermore, the detection capability of the model has been increased by introducing the small target detection layer which is developed by increasing the number of detection layers from three to four. The experimental results on multiple object detection dataset show that the proposed model achieves significant improvement in mean average precision (mAP) of almost 98% for various types of targets and an average precision (AP) of nearly 99% for small targets on the other hand it achieves 5.19% (mAP) and 9.79% (AP) compared to the YOLOv5s model. Furthermore, comparative analysis with other models like YOLOv7 and YOLOv8 shows that the proposed model has superior performance in terms of object detection.

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Liquid phase fluidity study of iron ore fines based on improved CondInst

Wang,

Li,

Liu

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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The liquid phase flowability of iron ore powder affects the quality of sintered ore. To better explore the liquid phase flow law of iron ore powder, this paper first adopted the improved CondInst (Conditional Convolutions for Instance Segmentation) to segment the image and achieved a segmentation accuracy of 96.61%. The segmentation accuracies on ResNet50 as well as ResNet101 were improved by 0.11% and 0.32%, respectively, relative to the original model. The image's height, area and wetting angle were used as the characteristic indexes of iron ore powder melting. The fitting curve was established by combining temperature and time to characterise the whole process of iron ore powder melting. Second, Factsage was used to simulate the liquid phase generation of iron ore powder, and a CatBoost regression model based on the liquid phase generation was constructed, and the maximum error between the predicted value and the real value was 3.74%. Finally, based on the equivalent mobility characteristic number, the liquid phase mobility performance of iron ore powder and the mechanism of alkalinity's influence on it were comprehensively analysed.

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Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5

Cited by 3 publications

References 32 publications

Reptile Search Algorithm with Deep Convolutional Neural Network for Cloud Assisted Colorectal Cancer Detection and Classification

Reptile Search Algorithm with Deep Convolutional Neural Network for Cloud Assisted Colorectal Cancer Detection and Classification

A new network model for multiple object detection for autonomous vehicle detection in mining environment

Liquid phase fluidity study of iron ore fines based on improved CondInst

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