Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection

Li, Jing; Gu, Jinan; Huang, Zedong; Jia, Wen Wei

doi:10.3390/app9183750

Cited by 117 publications

(67 citation statements)

References 21 publications

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“…We evaluate the proposed method on a dataset of PCB electronic components. There are 1000 images, 29 instrument categories, and 182900 electronic components in the dataset [ 15 ]. We first analyzed the ERF of each feature layer of the YOLOv3.…”

Section: Methodsmentioning

confidence: 99%

“…The mAP of electronic component detection on the testing PCBs can reach 65.3% [ 14 ]. Li et al proposed an improved YOLOv3 algorithm that added one output layer sensitive to small targets and validated the algorithm effectiveness in a real PCB picture and virtual PCB picture test, including many PCB electronic components [ 15 ]. Huang et al proposed a fast recognition method for electronic components in a stacked disordered scene.…”

Section: Introductionmentioning

confidence: 99%

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A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

Chen

et al. 2021

Computational Intelligence and Neuroscience

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Vision-based recognizing and positioning of electronic components on the PCB (printed circuit board) can improve the quality inspection efficiency of electronic products in the manufacturing process. With the improvement of the design and the production process, the electronic components on the PCB show the characteristics of small sizes and similar appearances, which brings challenges to visual object detection. This paper designs a real-time electronic component detection network through effective receptive field size and anchor size matching in YOLOv3. We make contributions in the following three aspects: (1) realizing the calculation and visualization of the effective receptive field size of the different depth layers of the CNN (convolutional neural network) based on gradient backpropagation; (2) proposing a modular YOLOv3 composition strategy that can be added and removed; and (3) designing a lightweight and efficient detection network by effective receptive field size and anchor size matching algorithm. Compared with the Faster-RCNN (regions with convolutional neural network) features, SSD (single-shot multibox detectors), and original YOLOv3, our method not only has the highest detection mAP (mean average precision) on the PCB electronic component dataset, which is 95.03%, the smallest parameter size of the memory, about 1/3 of the original YOLOv3 parameter amount, but also the second-best performance on FLOPs (floating point operations).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

Chen

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

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“…Deep learning has gradually become the mainstream in target detection after 2012 [ 15 ]. At present, many efficient target detection networks have been proposed and applied in the industrial field, such as yolo [ 16 – 19 ], Faster R-CNN [ 20 ], and NAS-FPN [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

A Robotic grinding station based on an industrial manipulator and vision system

2021

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Due to ever increasing precision and automation demands in robotic grinding, the automatic and robust robotic grinding workstation has become a research hot-spot. This work proposes a grinding workstation constituting of machine vision and an industrial manipulator to solve the difficulty of positioning rough metal cast objects and automatic grinding. Faced with the complex characteristics of industrial environment, such as weak contrast, light nonuniformity and scarcity, a coarse-to-fine two-step localization strategy was used for obtaining the object position. The deep neural network and template matching method were employed for determining the object position precisely in the presence of ambient light. Subsequently, edge extraction and contour fitting techniques were used to measure the position of the contour of the object and to locate the main burr on its surface after eliminating the influence of burr. The grid method was employed for detecting the main burrs, and the offline grinding trajectory of the industrial manipulator was planned with the guidance of the coordinate transformation method. The system greatly improves the automaticity through the entire process of loading, grinding and unloading. It can determine the object position and target the robotic grinding trajectory by the shape of the burr on the surface of an object. The measurements indicate that this system can work stably and efficiently, and the experimental results demonstrate the high accuracy and high efficiency of the proposed method. Meanwhile, it could well overcome the influence of the materials of grinding work pieces, scratch and rust.

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“…Such operation is repeatedly implemented until no changes to the clusters occur in several consecutive iterations and the clustering procedure finishes and converges as a result. Second, to perform the heuristic initialization, k-means++ clustering method is considered here [37], [38]. Compared to randomly specifying initial cluster centers, k-means++ method starts with the allocation of the first cluster center uniformly at random.…”

Section: Improved Anchor Boxes Generationmentioning

confidence: 99%

Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation

2020

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Visual perception is critical and essential to understand phenomenon and environments of the world. Pervasively configured devices like cameras are key in dynamic status monitoring, object detection and recognition. As such, visual sensor environments using one single or multiple cameras must deal with a huge amount of high-resolution images, videos or other multimedia. In this paper, to promote smart advancement and fast detection of visual environments, we propose a deep transfer learning strategy for real-time target detection for situations where acquiring large-scale data is complicated and challenging. By employing the concept of transfer learning and pre-training the network with established datasets, apart from the outstanding performance in target localization and recognition can be achieved, time consumption of training a deep model is also significantly reduced. Besides, the original clustering method, k-means, in the You Only Look Once (YOLOv3) detection model is sensitive to the initial cluster centers when estimating the initial width and height of the predicted bounding boxes, thereby processing large-scale data is extremely time-consuming. To handle such problems, an improved clustering method, mini batch k-means++ is incorporated into the detection model to improve the clustering accuracy. We examine the sustainable outperformance in three typical applications, digital pathology, smart agriculture and remote sensing, in vision-based sensing environments.

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Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection

Cited by 117 publications

References 21 publications

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

A Robotic grinding station based on an industrial manipulator and vision system

Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation

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