A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving
A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving
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DOI：
10.1109/access.2020.3033289
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Moon, Hyeonjoon
中科院分区：
文献类型：
--
作者：
Li, Yanfen;Wang, Hanxiang;Moon, Hyeonjoon
智能汽车作为智能交通系统的关键技术，是多种技术综合集成的载体。尽管基于视觉的自动驾驶已经显示出良好的前景，但如何通过采集的数据来分析复杂的交通状况仍然是一个问题。近年来，自主驾驶通过使用不同的模型被分别描述为多个任务，如目标检测任务和意图识别任务。在这项研究中，开发了一个基于视觉的系统，用于检测和识别交通场景中的各种目标，并预测行人的意图。本研究的主要贡献包括：(1)提出了一种基于YOLOv4结构的检测10种目标的优化模型；(2)提出了一种微调的部分亲和场方法来估计行人的姿态；(3)在风险评估阶段加入了可解释人工智能(XAI)技术来解释和辅助估计结果；(4)引入了一个精致的自动驾驶数据集，该数据集包含了每个相应任务的几个不同的子集；(5)开发了一个包含多个模型的端到端系统，该系统具有高精度。实验结果表明，优化后的YOLOv4总参数降低了74%，满足了系统的实时性要求。此外，优化的YOLOv4的检测精度比最先进的提高了2.6%。
As a key technology of intelligent transportation system, the intelligent vehicle is the carrier of comprehensive integration of many technologies. Although vision-based autonomous driving has shown excellent prospects, there is still a problem of how to analyze the complicated traffic situation by the collected data. Recently, autonomous driving has been formulated as many tasks separately by using different models, such as object detection task and intention recognition task. In this study, a vision-based system was developed to detect and identity various objects and predict the intention of pedestrians in the traffic scene. The main contributions of this research are (1) an optimized model was presented to detect 10 kinds of objects based on the structure of YOLOv4; (2) a fine-tuned Part Affinity Fields approach was proposed to estimate the pose of pedestrians; (3) Explainable Artificial Intelligence (XAI) technology is added to explain and assist the estimation results in the risk assessment phase; (4) an elaborate self-driving dataset that includes several different subsets for each corresponding task was introduced; and (5) an end-to-end system containing multiple models with high accuracy was developed. Experimental results proved that the total parameters of optimized YOLOv4 are reduced by 74% which satisfies the real-time capability. In addition, the detection precision of the optimized YOLOv4 achieved an improvement of 2.6%; compared to the state-of-the-art.