Structure of a semantic segmentation-based defect detection network for laser cladding infrared images

Deng, Shiyi; Gao, Ruipeng; Mao, Wei; Zheng, W.

doi:10.1088/1361-6501/acc7bd

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Techniques such as threshold-based segmentation [12], edge detection methods [13,14], region-growing techniques [15,16], clustering approaches [17], and their hybrids and advancements [18][19][20] are commonly used in 2D image-based phenotyping. Recent advances have seen convolutional neural networks (CNN) taking the forefront in tasks related to image classification and segmentation [21][22][23][24]. Deep learning frameworks have been utilized to differentiate between fruits and leaves in botanical images [23,25,26].…”

Section: Introductionmentioning

confidence: 99%

A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants

Peng,

Wang,

Zhang

et al. 2023

J. Imaging

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The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. In this study, we introduce a novel method for point-cloud downsampling that adeptly mitigates the challenges posed by sample imbalances. In subsequent developments, we architect a deep learning framework founded on the principles of SqueezeNet for the segmentation of plant point clouds. In addition, we also use the time series as input variables, which effectively improves the segmentation accuracy of the network. Based on semantic segmentation, the MeanShift algorithm is employed to execute instance segmentation on the point-cloud data of crops. In semantic segmentation, the average Precision, Recall, F1-score, and IoU of maize reached 99.35%, 99.26%, 99.30%, and 98.61%, and the average Precision, Recall, F1-score, and IoU of tomato reached 97.98%, 97.92%, 97.95%, and 95.98%. In instance segmentation, the accuracy of maize and tomato reached 98.45% and 96.12%. This research holds the potential to advance the fields of plant phenotypic extraction, ideotype selection, and precision agriculture.

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Section: Introductionmentioning

confidence: 99%

A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants

Peng,

Wang,

Zhang

et al. 2023

J. Imaging

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Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model

Zeng,

Peng,

Guo

et al. 2024

Meas. Sci. Technol.

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Laser Directed Energy Deposition (L-DED) has emerged as a promising technique for rapid prototyping due to its cost-effectiveness and efficiency. However, the intricate and multi-scale physics of the process hinder its wide-spread application. This paper addresses the challenge by focusing on real-time identification of melt pool states to detect defects early and minimize resource wastage. To achieve this, a FixConvNeXt model was developed for fast and accurate monitoring of melt pool states. This model was trained using 5000 melt pool images captured during the printing of single-track deposits from a Charge-Coupled Device(CCD). To evaluate its performance, FixConvNeXt was compared with other models using various metrics. Experimental results demonstrated that FixConvNeXt achieved superior performance in accurately identifying melt pool states with 99.1% accuracy, while also reducing computation burden and processing time. The mechanism of classification by FixConvNeXt was explained using Gradient-weighted Class Activation Mapping (Grad-CAM). The research findings highlight the potential application of online process monitoring in L-DED. This study lays the foundation for future development of an efficient deep learning network for automatic defect detection and feedback control.

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DDSNet: Deep Dual-Branch Networks for Surface Defect Segmentation

Yin,

Qin,

Han

et al. 2024

IEEE Trans. Instrum. Meas.

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Structure of a semantic segmentation-based defect detection network for laser cladding infrared images

Cited by 4 publications

References 24 publications

A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants

A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants

Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model

DDSNet: Deep Dual-Branch Networks for Surface Defect Segmentation

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