Test results for four types of defect detection.

<div><p>Traditional manual inspection approaches face challenges due to the reliance on the experience and alertness of operators, which limits their ability to meet the growing demands for efficiency and precision in modern manufacturing processes. Deep learning techniques, particularly...

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Bibliographic Details
Main Author:	Jianming Meng (22209039) (author)
Other Authors:	Longjian Guo (22209036) (author), Wei Hao (134390) (author), Deepak Kumar Jain (8171286) (author)
Published:	2025
Subjects:	Biochemistry Genetics Sociology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified small target defects shown significant promise modern manufacturing processes model &# 8217 inner_mpdiou loss function earlier yolo versions detecting small defects dependable automated solution deep learning techniques across varying scales achieved significant improvements generalization tests conducted improved yolov11 model improved yolov11 achieves detection accuracy improved yolov9 &# 8217 significantly higher precision handling complex backgrounds existing yolo models surface defect detection recall rate improvement electronic products based 3 %, respectively 6 %, recall improved yolov11 electronic products complex backgrounds defect detection recall rate based method 1 %, resistor detection object detection detection speed various applications precision increase pcb dataset paper proposes growing demands generalization ability findings emphasize dualconv module c2f module 6 %. 2 %,
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Test results for four types of defect detection.

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