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基于Darknet網(wǎng)絡(luò )和YOLO4的實(shí)時(shí)電路板故障檢測算法
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國營(yíng)長(cháng)虹機械廠(chǎng)

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Real-time PCB fault detection algorithm based on Darknet network and YOLO4
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    摘要:

    針對現有的接觸式電路板故障檢測方法難以應用到大規模集成電路故障檢測中的問(wèn)題,提出一種實(shí)時(shí)、非接觸式的基于深度學(xué)習的電路板故障診斷算法。建立PCB板缺陷檢測和元器件識別圖像數據集,并采用數據增強技術(shù),對數據進(jìn)行數據增強來(lái)提高訓練的數據量,以提升模型檢測精度和魯棒性;基于Darknet框架和YOLO4算法訓練得到元器件檢測模型,并通過(guò)采用k-means聚類(lèi)算法設計合理的Anchors,使得模型具備多尺度缺陷檢測的功能;使用圖像配準算法在紅外圖像和可見(jiàn)光圖像上實(shí)現配準和融合。根據PCB板設計時(shí)劃分的功能區域,利用測溫熱像儀連續采集5個(gè)該區域的平均溫度,通過(guò)判斷5個(gè)平均溫度之間的關(guān)系從而判斷短路或者短路狀態(tài)。經(jīng)過(guò)試驗測試,使用預先設置好故障的電路板作為實(shí)驗對象,通過(guò)采集實(shí)驗對象運行過(guò)程中的紅外和可見(jiàn)光圖像數據,基于設計的故障檢測模型,不僅能夠實(shí)時(shí)且有效的識別出元器件位置,并能夠直觀(guān)的標識出現短路、短路故障元器件。經(jīng)過(guò)實(shí)際應用,能夠滿(mǎn)足設備運行時(shí)的實(shí)時(shí)電路板故障檢測工程應用。

    Abstract:

    A real-time, non-contact circuit boards fault diagnosis algorithm based on deep learning is presented to solve the problem that existing contact circuit board fault detection methods is difficult be applied to large scale integrated circuit fault detection. Establish an image data set of PCB board defect detection and component recognition, and adopt data enhancement technology to enhance the data volume of training to improve the accuracy and robustness of model detection.Component detection model is got by training based on Darknet framework and YOLO4 algorithm, and reasonable Anchors is designed by K-means clustering algorithm to make the model have multi-scale defect detection function. Image registration algorithms are used to register and fuse infrared and visible images. According to the functional area divided by PCB board design, the average temperature of five areas is collected continuously by thermometry thermal imager, and the short circuit or short circuit status is judged by judging the relationship between the five average temperatures. After testing, using pre-set faulty circuit board as the experimental object, by collecting infrared and visible image data during the operation of the experimental object, based on the designed fault detection model, not only the real-time and effective identification of component location, but also the intuitive identification of components with short-circuit and short-circuit faults.After practical application, it can satisfy the engineering application of real-time circuit board fault detection when the equipment is running.

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趙巖,孔祥偉,馬春斌,楊浩.基于Darknet網(wǎng)絡(luò )和YOLO4的實(shí)時(shí)電路板故障檢測算法計算機測量與控制[J].,2023,31(6):101-108.

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  • 收稿日期:2023-03-14
  • 最后修改日期:2023-03-27
  • 錄用日期:2023-03-28
  • 在線(xiàn)發(fā)布日期: 2023-06-15
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