The network structure of our model.

<div><p>Aiming at the problems of low accuracy and large computation in the task of PCB defect detection. This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Shiwei Yu (6060308) (author)
مؤلفون آخرون:	Feng Pan (257056) (author), Xiaoqiang Zhang (2413909) (author), Linhua Zhou (6430619) (author), Liang Zhang (41392) (author), Jikui Wang (4519225) (author)
منشور في:	2025
الموضوعات:	Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> aiming simple defect types pcb defect detection lightweight detection algorithm depthwise separable convolution redundant parameters within model &# 8217 yolo </ p panet network structure ordinary convolution model lightweight scale features results indicated parameter count paper proposes original model large numbers large computation defective images confusion caused cluttered background c3str module c3 module better suited arithmetic platforms
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

The network structure of our model.

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