Performance comparison of YOLOv8n adding different loss functions.

<p>Performance comparison of YOLOv8n adding different loss functions.</p>

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Bibliographic Details
Main Author:	Wenyuan Xu (2553028) (author)
Other Authors:	Jianbo Xu (1824238) (author), Yongcheng Ji (5628128) (author), Guodong Li (1525873) (author), Hao Li (31608) (author), Zhen Zang (684900) (author)
Published:	2025
Subjects:	Biochemistry Biotechnology Cancer Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified spatial pyramid pooling efficiently extracts multi detrimental gradients produced complex background interference 50 &# 8211 8 %, respectively target scale variability stage partial convolution stage partial channel insufficient detection accuracy improved yolov8n algorithm benchmark model yolov8n 8 %, 1 stage partial detection accuracy enhanced yolov8n 7 %, scale features enhanced algorithm algorithm introduces xlink "> weighted intersection significantly enhanced road fractures quality pictures neck paradigm model parameters loss function learning capability leakage due fine slim feature fusion experimental outcomes employed instead effectively reducing effectively accommodate complete intersection average precision attention mechanism aforementioned enhancements 95 ),
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