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. 2024 Feb 23;10(5):e26532.
doi: 10.1016/j.heliyon.2024.e26532. eCollection 2024 Mar 15.

YOLOv5 based object detection in reel package X-ray images of semiconductor component

Affiliations

YOLOv5 based object detection in reel package X-ray images of semiconductor component

Jinwoo Park et al. Heliyon. .

Abstract

The industrial manufacturing landscape is currently shifting toward the incorporation of technologies based on artificial intelligence (AI). This transition includes an evolution toward smart factory infrastructure, with a specific focus on AI-driven strategies in production and quality control. Specifically, AI-empowered computer vision has emerged as a potent tool that offers a departure from extant rule-based systems and provides enhanced operational efficiency at manufacturing sites. As the manufacturing sector embraces this new paradigm, the impetus to integrate AI-integrated manufacturing is evident. Within this framework, one salient application is AI deep learning-facilitated small-object detection, which is poised to have extensive implications for diverse industrial applications. This study describes an optimized iteration of the YOLOv5 model, which is known for its efficacious single-stage object-detection abilities underpinned by PyTorch. Our proposed "improved model" incorporates an additional layer to the model's canonical three-layer architecture, augmenting accuracy and computational expediency. Empirical evaluations using semiconductor X-ray imagery reveal the model's superior performance metrics. Given the intricate specifications of surface-mount technologies, which are characterized by a plethora of micro-scale components, our model makes a seminal contribution to real-time, in-line production assessments. Quantitative analyses show that our improved model attained a mean average precision of 0.622, surpassing YOLOv5's 0.349, and a marked accuracy enhancement of 0.865, which is a significant improvement on YOLOv5's 0.552. These findings bolster the model's robustness and potential applicability, particularly in discerning objects at reel granularities during real-time inferencing.

Keywords: Artificial intelligence; Semiconductor; Small object detection; X-ray; YOLOv5.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Semiconductor reel.
Figure 2
Figure 2
Reel taping.
Figure 3
Figure 3
X-ray machine, applications.
Figure 4
Figure 4
YOLOv5 model.
Figure 5
Figure 5
CSP DenseNet.
Figure 6
Figure 6
YOLOv5 SPP architecture.
Figure 7
Figure 7
Performance of YOLOv5.
Figure 8
Figure 8
Improved YOLOv5 model.
Figure 9
Figure 9
X-ray images of semiconductor reel.
Figure 10
Figure 10
X-ray images and labelled data.
Figure 11
Figure 11
Labelled datasets for trainning.
Figure 12
Figure 12
Chip counting (class 1, 3, 4).
Figure 13
Figure 13
Chip counting (class 2, 5, 6, 7, 8).
Figure 14
Figure 14
Chip counting (class 9, 10).

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