Center detection algorithm for printed circuit board circular marks based on image space and parameter space

Qi, Min; Wang, Yanan; Chen, Yanshuo; Xin, Hongjuan; Xu, Yansheng; Meng, Hongying; Wang, Aili

doi:10.1117/1.jei.32.1.011002

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In complex backgrounds, the model of YOLOv5s was more accurate than the detection of Hoff variations and was faster. In order to solve the problems of large memory overhead, long time consumption and low recognition accuracy of offset Hough transform, slam, N. et al [76] proposed an efficient circle localization algorithm based on multi-resolution segmentation (two-step optimized Hough transform). First, the target circle was obtained by adaptive image preprocessing to determine the location of the effective search area.…”

Section: Spatial Context Featurementioning

confidence: 99%

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.

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Section: Spatial Context Featurementioning

confidence: 99%

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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“…However, the key to the detection method is just a specific state. Therefore, relying on the original anchor boxes dimensions does not meet the practical needs of PCB defect detection [12,13].…”

Section: Problem Statementmentioning

confidence: 99%

Detection Algorithm of Surface Defect Word on Printed Circuit Board

Zhang¹,

Xi²

2023

Computer Systems Science and Engineering

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For Printed Circuit Board (PCB) surface defect detection, traditional detection methods mostly focus on template matching-based reference method and manual detections, which have the disadvantages of low defect detection efficiency, large errors in defect identification and localization, and low versatility of detection methods. In order to further meet the requirements of high detection accuracy, real-time and interactivity required by the PCB industry in actual production life. In the current work, we improve the Youonly-look-once (YOLOv4) defect detection method to train and detect six types of PCB small target defects. Firstly, the original Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is preserved for PCB defect feature information extraction, and secondly, the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process. Then, the K-means++ clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection, which further improves the recognition and localization of small PCB defects. Finally, the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms. The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%, which has better detection capability, lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.

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