(a) YOLOv 8s, (b) YOLOv 7-tiny, (c) YOLOv 5l, and (d) SEA-YOLO v8.

(a) YOLOv 8s, (b) YOLOv 7-tiny, (c) YOLOv 5l, and (d) SEA-YOLO v8.

<p>(a) YOLOv 8s, (b) YOLOv 7-tiny, (c) YOLOv 5l, and (d) SEA-YOLO v8.</p>

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Bibliographic Details
Main Author: Yuxi Zhao (1797436) (author)
Other Authors: Baoyong Shi (21568164) (author), Xiaoguang Duan (1554544) (author), Wenxing Zhu (14589422) (author), Liying Ren (11143129) (author), Chang Liao (564787) (author)
Published: 2025
Subjects:
Ecology
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
utilize feature information
rdd2022 dataset show
improve feature representation
feature fusion capability
road damage detection
road damage assessment
detect road damage
limited hardware resources
yolo v8 model
time target detection
high detection accuracy
study constructed sea
material resources
road maintenance
yolov8 model
traffic safety
sustainable development
selectively focus
sbs module
save manpower
provide guidance
neck component
key areas
great significance
experimental results
different layers
computational complexity
achieved 63
accurately locate
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Summary:<p>(a) YOLOv 8s, (b) YOLOv 7-tiny, (c) YOLOv 5l, and (d) SEA-YOLO v8.</p>

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