计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 240100138-5.doi: 10.11896/jsjkx.240100138
李婷, 赵尔敦, 杨军
LI Ting, ZHAO Erdun, YANG Jun
摘要： 随着科技的飞速发展,辅助驾驶技术已经成为汽车行业未来发展的重要方向。在基于图像的道路障碍物检测中,现有方法对尺度变化大的目标、小目标和存在遮挡目标的检测能力有限,常出现误判和漏判等问题。针对此问题,提出了一种基于自注意力与双向特征融合的道路障碍物检测方法(CoXt-FCOS)。该方法在主干特征提取网络中引入分组的自注意力机制模块CoXT,以增强网络的全局信息捕获能力;为解决遮挡问题,引入跨阶段金字塔池化模块SPPCSPC;在特征融合模块中,引入路径增强网络,形成双向特征融合模块ESPAFPN,提升网络对小目标的感知能力。实验结果表明,CoXT-FCOS模型的精度较高,在CODA数据集上的mAP达到了88%,能够更准确地检测出道路上的障碍物。
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