基于感受野增强的复杂道路场景目标检测研究
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1.无锡学院江苏省集成电路可靠性技术及检测系统工程研究中心 无锡 214105; 2.南京信息工程大学电子与信息工程学院 南京 210044

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TP391.4; TN914

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国家自然科学基金(62204172)项目资助


Research on target detection in complex road scenes based on receptive field enhancement
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1.Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System, Wuxi University, Wuxi 214105, China; 2.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

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    摘要:

    针对当前自动驾驶场景下道路目标检测算法对远距离小目标、遮挡目标容易出现漏检和误检的问题,提出一种基于改进YOLOv8n的道路目标检测算法。在特征提取方面,对感受野注意力卷积进行轻量化改进,重新构造C2f模块,以解决卷积计算中参数无法共享问题,使网络有效捕捉关键信息;然后引入轻量化点采样算子,以减少上采样过程中特征细节损失,更好保留图像的细节信息;在特征融合方面,设计多尺度特征融合网络,以增强小目标特征信息,丰富不同尺度特征的双向融合;同时使用归一化注意力机制,以抑制无关背景信息干扰,提高模型抗干扰能力。实验结果表明,提出的改进算法在KITTI数据集和Udacity数据集上的检测精度分别达到了92.8%和78.7%,相比原始算法分别提高了2.2%和1.6%,模型依然满足轻量化要求,在一定程度上提高了对复杂道路场景的适应能力。

    Abstract:

    To address the issue of missed and false detections for distant small objects and occluded objects in current road target detection algorithms in autonomous driving scenarios, a road target detection algorithm based on an improved YOLOv8n is proposed. In terms of feature extraction, the Receptive-Field Attention Convolution is lightweightly improved, and the C2f module is reconstructed to solve the problem of non-shared parameters in convolution calculations, enabling the network to effectively capture critical information. Then, a lightweight point sampling operator is introduced to reduce the loss of feature details during the upsampling process, better preserving image detail information. In terms of feature fusion, a multi-scale feature fusion network is designed to enhance small target feature information and enrich the bidirectional fusion of features at different scales. Simultaneously, a normalization attention mechanism is used to suppress irrelevant background information interference, improving the model′s anti-interference capability. Experimental results show that the proposed improved algorithm achieves detection accuracies of 92.6% and 78.7% on the KITTI dataset and the Udacity dataset, respectively, representing improvements of 2.1% and 1.6% compared to the original algorithm. The model still meets lightweight requirements and enhances adaptability to complex road scenes to a certain extent.

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刘罡,闫曙光,刘钰,侯恩翔,黄应征.基于感受野增强的复杂道路场景目标检测研究[J].电子测量技术,2024,47(13):157-166

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  • 在线发布日期: 2024-11-07
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