The network structure of FasterNet--YOLO.

<div><p>Steel surface defect detection is an important application of object detection in industry. Achieving object detection in industry while balancing detection accuracy and real-time performance is a challenge. Therefore, this paper proposes an improved FasterNet-YOLO model based on...

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Bibliographic Details
Main Author:	Shiwei Yu (6060308) (author)
Other Authors:	Zelin Liu (2077912) (author), Liang Zhang (41392) (author), Xiaoqiang Zhang (2413909) (author), Jikui Wang (4519225) (author)
Published:	2025
Subjects:	Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified also significantly improves detector &# 8217 depthwise separable convolution 4 %, gflops yolov5 backbone network reducing redundant parameters achieving object detection balancing detection accuracy improve model accuracy improve detection speed yolo model based object detection detection accuracy stage detector ordinary convolution 2 %, time detection detection speed original model model lightweighting neck network fasternet network improved model time performance slight reduction significant improvement results indicated paper proposes informative features important application fps increased feature fusion easy confusion different scales defect types defect photographs cluttered backgrounds c3 module 1 %.
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The network structure of FasterNet--YOLO.

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