计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 256-263.doi: 10.11896/jsjkx.200700026
龚航, 刘培顺
GONG Hang, LIU Pei-shun
摘要： 有效地对夜间车辆违规使用远光灯的行为进行管理,可以降低夜间交通事故的发生,但目前缺乏高效的远光灯检测方法,相关交通法规无法得到有效执行。针对此问题,文中提出了一种夜间车辆远光灯检测深度学习算法。该算法基于YOLOv3进行设计,通过降低各层卷积层维数的方式,来减少整体网络的参数量,提高算法的运行速度;然后对网络的残差组件进行改进,使用标准的残差组件,同时设计了一个空洞卷积模块来加强网络局部和全局特征的融合,增强了网络的特征表达能力;接着对YOLOv3的损失函数进行了改进,优化小尺寸目标对坐标损失的贡献,增强了小尺度目标的检测能力;最后对YOLOv3先验框聚类算法和个数进行优化,提高模型的表达能力和检测速度。实验结果表明,所设计的算法的平均准确率(mAP)达到了99.09%,相比YOLOv3提升了30%,满足了实用化要求,能够有效地检测违规行为。
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