A lightweight detector based on attention mechanism for aluminum strip surface defect detection

Ma, Zhuxi; Li, Yibo; Huang, Minghui; Huang, Qianbin; Cheng, Jie; Tang, Si Yuan

doi:10.1016/j.compind.2021.103585

Cited by 57 publications

(19 citation statements)

References 34 publications

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“…The original team of YOLOv4 proposed scaled-YOLOv4 based on YOLOv4-CSP to make the model can be developed on different devices [ 25 ]. To further improve the performance of YOLOv4, some improved methods based on YOLOv4 have also been proposed [ 26 – 28 ]. To improve the one-stage method performance, Glenn proposed YOLOv5 based on YOLOv3 [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

“…proposed scaled-YOLOv4 based on YOLOv4-CSP to make the model can be developed on different devices [25]. To further improve the performance of YOLOv4, some improved methods based on YOLOv4 have also been proposed [26][27][28]. To improve the one-stage method performance, Glenn proposed YOLOv5 based on YOLOv3 [10] Due to the advantage of the YOLO series method, they have been widely used in transmission devices and defective insulator detection.…”

Section: Related Workmentioning

confidence: 99%

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Fast Detection of Defective Insulator Based on Improved YOLOv5s

Zhao

Mengjun

Cui

et al. 2022

Computational Intelligence and Neuroscience

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Defective insulator detection is an essential part of transmission line inspections based on unmanned aerial vehicles. It can timely discover insulator defects and repair them to avoid a power transmission accident. The detection speed of defective insulators based on artificial intelligence directly affects inspection efficiency. To improve the detection speed of defective insulators based on YOLOv5s, an improved detection method with faster detection speed and acceptable precision is proposed. First, a new ResNet unit with three branches is designed based on depthwise separable convolution with kernel three and average pooling. To reduce parameters, the new ResNet unit is used to replace the original ResNet unit used in the CSP1_X module in YOLOv5s. Besides, we also introduce channel shuffle in the CSP1_X module to facilitate the flow of feature information from different channels. Second, a new residual CBL module is designed based on depthwise separable and standard convolution. The new residual CBL module is used to replace the two CBL modules used in the CSP2_X module in YOLOv5s to reduce parameters and extract more useful features. Third, we design a separate, coordinated attention module by introducing location information into channel attention. The new attention module is added to the end of the CSP2_X module to improve the ability to extract insulator location information. Besides, we also use convolution to replace the focus model to reduce computation. Compared with defective insulator detection methods, the proposed method has smaller parameters, floating-point operations per second, and higher frames per second. Although it has lower mean precision, it has a faster detection speed. Besides, the increase in detection speed is greater than the decrease in mean precision.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fast Detection of Defective Insulator Based on Improved YOLOv5s

Zhao

Mengjun

Cui

et al. 2022

Computational Intelligence and Neuroscience

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“…Te object bounding box estimation mechanisms of R-CNN [22], Fast R-CNN [23], Faster R-CNN [24], SSD [25], and YOLO [26], are wellknown algorithms. In industrial applications, faster detection and segmentation mechanisms are increasingly used in domains, such as defect/weld image surface inspections [27][28][29][30], manufacturing/textile line quality analysis [31], smart crops and farming [32], semiconductor fabrication and design processes [33], and engineering condition monitoring fault diagnosis owing to their faster and more accurate performance [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Instance Segmentation Models for Identification of Vehicle Parts

et al. 2023

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Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for A P I o U = . 50 and A P I o U = . 75 reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for A P s with 44.0 and 2.6 for object detection and segmentation categories, respectively.

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“…MA Z. proposed a dual‐channel attention module with deep separable convolution and applied it to the YOLOv4 model for aluminum strip profile detection. [ 33 ] The experiments showed that the detection speed increased by three times, the average accuracy reached 96.28%, and the volume of the model was reduced by 83.38%. Domingo M. showed that YOLO‐based deep learning detection methods have a high level of performance in the detection of X‐ray images of defective aluminum castings.…”

Section: Introductionmentioning

confidence: 99%

BHE‐YOLO: Effective Small Target Detector for Aluminum Surface Defect Detection

Xu,

Hu,

Zhang

et al. 2023

Advcd Theory and Sims

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Aluminum surface defect detection is a critical task to make location and classification predictions about defects in the industrial production process. However, defects complexity and the requirement of rapid production have led to a great challenge for existing detection algorithms. In this work, an effective small target detector named BHE‐YOLO is proposed for aluminum surface defect detection. First, the BiFPN is modified and integrated with YOLOv5s to achieve effective weighted feature fusion and cross‐scale connection. Second, the Hard Swish activation function is applied to better extract defect feature information. Third, the Equalized Focal Loss function is introduced to replace the cross‐entropy formula of the negative sample confidence part of the loss function. Finally, the experiments are carried out on the aluminum profile defect dataset of the Aliyun Tianchi Competition. The results demonstrate that BHE‐YOLO has excellent performance in terms of Precision, Recall, F1 score, and mAP, and its superiority over the typical detection algorithms, especially for small target defects of aluminum surface.

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A lightweight detector based on attention mechanism for aluminum strip surface defect detection

Cited by 57 publications

References 34 publications

Fast Detection of Defective Insulator Based on Improved YOLOv5s

Fast Detection of Defective Insulator Based on Improved YOLOv5s

Real-Time Instance Segmentation Models for Identification of Vehicle Parts

BHE‐YOLO: Effective Small Target Detector for Aluminum Surface Defect Detection

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