A Lightweight Modified YOLOX Network Using Coordinate Attention Mechanism for PCB Surface Defect Detection

Wang, Xuan; Jin, Gao; Bo-jie, Hou; Wang, Zongshan; Hong-wei, Ding; Abbasi, Saad Jamshed

doi:10.1109/jsen.2022.3208580

Cited by 48 publications

(16 citation statements)

References 29 publications

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“…In numerous industrial defect detection scenarios, the depthwise separable convolution (DSC) [ 33 ] is widely introduced into the detection networks for reducing the computational resource and feature redundancy [ 34 , 35 , 36 ], so as to meet the real-time requirement of industry production process. In this article, the DSC is adopted to constitute the designed Res-DSC module.…”

Section: Methodsmentioning

confidence: 99%

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

Zhu,

Xu,

Lin

et al. 2024

Sensors

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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.

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Section: Methodsmentioning

confidence: 99%

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

Zhu,

Xu,

Lin

et al. 2024

Sensors

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“…Dou et al [21] added the NAM module to YOLOv5, resulting in a 17% increase in computational speed. Xuan et al [22] added the CA module to YOLOX and applied it to defect detection in printed circuit boards (PCB), achieving improved detection performance.…”

Section: Attention Mechanismmentioning

confidence: 99%

Steel Surface Defect Detection Using Improved Deep Learning Algorithm: ECA-SimSPPF-SIoU-Yolov5

Ren,

Fei,

et al. 2024

IEEE Access

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Steel surface defect detection is an indispensable part of industrial production and processing processes. It helps to reduce production costs, ensure product quality, improve production safety and compliance, and maintain sustainability and competitiveness. To address the low detection accuracy of traditional methods, this paper developed and investigated an improved algorithm based on YOLO for steel surface defect detection: ECA(Efficient Channel Attention) -SimSPPF (Simplified Spatial Pooling -Fast) -SIoU (Scylla Intersection over Union) -Yolov5. First, deformable convolutions were used to replace some conventional convolutions in the model, which expanded the receptive field and improved detection accuracy. Additionally, Efficient Channel Attention was integrated into the model to improve the weight of important information. Then, the SimSPPF was employed in place of the SPP module in the model, reducing computational complexity. Finally, the SIoU loss function was utilized to handle bounding box regression more effectively. The paper conducted different ablation experiments, and the improved ECA-SimSPPF-SIoU-Yolov5 algorithm demonstrated superior detection performance. Using the NEU-DET dataset, the mAP reached 78.8%, which was a 7.1% improvement higher than the original model, while the Recall reached 76.4% and improved by 3.7% compared to the original model. The improved model showed significant improvements in terms of mAP and Recall. Furthermore, the paper conducted multiple comparative experiments, comparing the model with other attention mechanisms and loss functions. The results demonstrated that the improved ECA-SimSPPF-SIoU-Yolov5 algorithm achieved good detection results in terms of mAP and Recall. In the third comparative experiment, the model was compared with YOLOv5 model with different network depths and the latest Yolov8 model, and the improved model also achieved good detection accuracy.

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“…For the anomaly localization task, we adopt the precision, recall, and F 1 scores for quantitative performance evaluation precision = TP TP + FP (8) recall = TP TP + FN ( 9)…”

Section: Table II Details Of the Irsa Datasetmentioning

confidence: 99%

“…According to the degree of label usage, deep-learning-based anomaly detection (DAD) models can be divided into supervised, semisupervised, and unsupervised learning models. The supervised models [6], [7], [8], [9] require detailed labels (e.g., categories, rectangular boxes, and pixelwise annotations) for various abnormal samples, which limit their uses in railway safety applications. The semisupervised models [10], [11] fully consider the inherent characteristics of the data, and they separate outliers by learning commonalities in data.…”

Section: Introductionmentioning

confidence: 99%

Intercity Railway Risk Space Anomaly Detection Based on Train Predeparture Key Frame Extraction and IADN Network

Duan

Liu

et al. 2023

IEEE Sensors J.

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Anomalies between the platform doors and train seriously threaten the safe operation of intercity railway train, and therefore, accurately selecting the appropriate time for anomaly detection in elaborate process of train access is vital. However, the scarcity of data samples, disadvantage of anomaly reconstruction, and unreliable reconstruction errors pose challenges to image reconstruction-based anomaly detection methods. To address these issues, this study proposes a method based on train predeparture key frame extraction and patch-level unsupervised network using image inpainting. The key frame extraction is designed to extract the key frame to assist subsequent detection by tracking the motion state of train door and converting states into the switching door signals. An unsupervised network, which named image-inpainting anomaly detection network (IADN), based on image-inpainting autoencoder (AE), local abnormal information enhancement, and global-attentive reconstruction error (GARE), is proposed for anomaly classification and localization. The network utilizes an image-inpainting AE based on masking strategy and dual-channel structure to reconstruct normal images and solve the anomaly reconstruction problem. Afterward, it proposes local abnormal information enhancement and GARE to enhance the difference between inputs and reconstructions to improve the accuracy. The proposed method is tested on the intercity railway risk space anomaly dataset, and the anomaly classification AUC, localization precision, and recall achieve 99.32%, 97.45%, and 96.29%, respectively, which outperforms state-of-the-art methods.

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A Lightweight Modified YOLOX Network Using Coordinate Attention Mechanism for PCB Surface Defect Detection

Cited by 48 publications

References 29 publications

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

Steel Surface Defect Detection Using Improved Deep Learning Algorithm: ECA-SimSPPF-SIoU-Yolov5

Intercity Railway Risk Space Anomaly Detection Based on Train Predeparture Key Frame Extraction and IADN Network

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