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Volume 13
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10.3390/electronics13204121
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Article

AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8

by
Donghao Wu
,
Chao Fang
*,
Xiaogang Zheng
,
Jue Liu
,
Shengchun Wang
and
Xinyu Huang
School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(20), 4121; https://doi.org/10.3390/electronics13204121
Submission received: 13 September 2024 / Revised: 13 October 2024 / Accepted: 16 October 2024 / Published: 19 October 2024
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Abstract

This study introduces an improved YOLOv8 model tailored for detecting objects in road scenes. To overcome the limitations of standard convolution operations in adapting to varying targets, we introduce Adaptive Kernel Convolution (AKconv). AKconv dynamically adjusts the convolution kernel’s shape and size, enhancing the backbone network’s feature extraction capabilities and improving feature representation across different scales. Additionally, we employ a Multi-Scale Dilated Attention (MSDA) mechanism to focus on key target features, further enhancing feature representation. To address the challenge posed by YOLOv8’s large down sampling factor, which limits the learning of small target features in deeper feature maps, we add a small target detection layer. Finally, to improve model training efficiency, we introduce a regression loss function with a Wise-IoU dynamic non-monotonic focusing mechanism. With these enhancements, our improved YOLOv8 model excels in road scene object detection tasks, achieving a 5.6 percentage point improvement in average precision over the original YOLOv8n on real road datasets.
Keywords: road scene detection; YOLOv8; adaptive kernel convolution; multi-scale dilated attention; small target detection road scene detection; YOLOv8; adaptive kernel convolution; multi-scale dilated attention; small target detection

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MDPI and ACS Style

Wu, D.; Fang, C.; Zheng, X.; Liu, J.; Wang, S.; Huang, X. AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8. Electronics 2024, 13, 4121. https://doi.org/10.3390/electronics13204121

AMA Style

Wu D, Fang C, Zheng X, Liu J, Wang S, Huang X. AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8. Electronics. 2024; 13(20):4121. https://doi.org/10.3390/electronics13204121

Chicago/Turabian Style

Wu, Donghao, Chao Fang, Xiaogang Zheng, Jue Liu, Shengchun Wang, and Xinyu Huang. 2024. "AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8" Electronics 13, no. 20: 4121. https://doi.org/10.3390/electronics13204121

APA Style

Wu, D., Fang, C., Zheng, X., Liu, J., Wang, S., & Huang, X. (2024). AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8. Electronics, 13(20), 4121. https://doi.org/10.3390/electronics13204121

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