Fast Traffic Accident Identification Method Based on SSD Model

Haiyang Jiang, Yuning Wang, Yong Yang
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引用次数: 3

Abstract

Traditional traffic accident identification methods have the problems of complex detection process, poor detection performance and poor real-time performance so far. In this paper, we propose a new type of traffic accident identification method based on target detection algorithm Single Shot MultiBox Detector (SSD). We collect and simulate traffic accident data sets in different scenarios and compare the detection performance of different target detection algorithms, aiming at the problems of traffic accident detection existing in the original SSD, the idea of multi-feature fusion and adaptive default box selection algorithm are proposed to improve it. Finally, we present an evaluation on the collected data, the improved SSD_A method shows considerable performance, which can reach 97% mAP (mean average precision) at the speed of 32 FPS (frames per second).
基于SSD模型的交通事故快速识别方法
传统的交通事故识别方法目前存在检测过程复杂、检测性能差、实时性差等问题。本文提出了一种基于目标检测算法单弹多盒检测器(Single Shot MultiBox Detector, SSD)的交通事故识别方法。我们采集并模拟了不同场景下的交通事故数据集,比较了不同目标检测算法的检测性能,针对原有SSD存在的交通事故检测问题,提出了多特征融合和自适应默认框选择算法的思想对其进行改进。最后,我们对采集到的数据进行了评估,改进的SSD_A方法显示出相当好的性能,在32 FPS(帧/秒)的速度下可以达到97%的mAP(平均平均精度)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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