计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 205-211.doi: 10.11896/jsjkx.190900078
黄同愿, 杨雪姣, 向国徽, 陈辽
HUANG Tong-yuang, YANG Xue-jiao, XIANG Guo-hui, CHEN Liao
摘要： 自动驾驶场景下,为了提高远距离行人的检测精度和测距精度,结合深度学习的目标检测,提出了一种行人测距算法。首先,提出了冗余切图法,结合YOLOV3模型对小目标行人进行检测,再通过改进的边界框筛选算法对所有子图的候选框进行多次筛选,最终得到行人检测框。然后,对传统的相似三角形测距算法进行分析,提出了一种包含pitch和yaw的改进相似三角形测距算法。最后,根据行人检测结果实时测量行人距离当前车辆的横向距离和纵向距离。实验结果表明,在BDD 100K验证集上,所提出的冗余切图法检测模型比原YOLOV3模型的mAP提高了6%,比小目标行人的mAP提高了3%,具有更好的检测鲁棒性;在车载摄像头采集的测距测试集上,冗余切图法和改进的测距算法的结合使测距精度在对比实验结果上改善了6.542%,不仅实现了远距离测距,而且具有更高的测距准确性。
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