WGS-YOLO:基于 YOLO 框架的自动驾驶实时物体检测器

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Vision and Image Understanding Pub Date : 2024-10-03 DOI:10.1016/j.cviu.2024.104200
Shiqin Yue , Ziyi Zhang , Ying Shi , Yonghua Cai
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引用次数: 0

摘要

自动驾驶的安全性和可靠性取决于物体检测系统的精度和效率。本文对 YOLO 架构(WGS-YOLO)进行了改进,以提高行人和车辆的检测能力。具体来说,通过加入加权高效层聚合网络(Weighted Efficient Layer Aggregation Network,W-ELAN)模块(一种使用信道洗牌的创新动态加权特征融合模块),增强了其信息融合能力。同时,通过采用战略性设计的空深卷积(SPD-Conv)和分组空间金字塔池化(GSPP)模块,大大降低了拟议 WGS-YOLO 的计算需求和参数。我们使用 BDD100k 和 DAIR-V2X-V 数据集评估了模型的性能。就平均精度(mAP0.5)而言,所提出的模型比基准 Yolov7 高出 12%。此外,我们还进行了大量实验,以验证我们的分析和模型在不同场景下的鲁棒性。
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WGS-YOLO: A real-time object detector based on YOLO framework for autonomous driving
The safety and reliability of autonomous driving depends on the precision and efficiency of object detection systems. In this paper, a refined adaptation of the YOLO architecture (WGS-YOLO) is developed to improve the detection of pedestrians and vehicles. Specifically, its information fusion is enhanced by incorporating the Weighted Efficient Layer Aggregation Network (W-ELAN) module, an innovative dynamic weighted feature fusion module using channel shuffling. Meanwhile, the computational demands and parameters of the proposed WGS-YOLO are significantly reduced by employing the Space-to-Depth Convolution (SPD-Conv) and the Grouped Spatial Pyramid Pooling (GSPP) modules that have been strategically designed. The performance of our model is evaluated with the BDD100k and DAIR-V2X-V datasets. In terms of mean Average Precision (mAP0.5), the proposed model outperforms the baseline Yolov7 by 12%. Furthermore, extensive experiments are conducted to verify our analysis and the model’s robustness across diverse scenarios.
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
期刊最新文献
Editorial Board Multi-Scale Adaptive Skeleton Transformer for action recognition Open-set domain adaptation with visual-language foundation models Leveraging vision-language prompts for real-world image restoration and enhancement RetSeg3D: Retention-based 3D semantic segmentation for autonomous driving
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