计算机科学
雷达
人工智能
计算机视觉
目标检测
雷达成像
对象(语法)
遥感
雷达工程细节
探测器
便携式雷达
模式识别(心理学)
地理
电信
作者
Atsutake Kosuge,Satoshi Suehiro,Mototsugu Hamada,Tadahiro Kuroda
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:71: 1-10
被引量:17
标识
DOI:10.1109/tim.2022.3176014
摘要
This paper presents a mmWave imaging radar-based real-time multi-class object recognition system for ADAS applications related to construction machinery. While mmWave radar has the advantage of being able to detect signals even in the optically harsh, dark, and dusty environments in which construction machinery is often used, the radar has two orders of magnitude lower resolution than cameras. Since the distance from the radar increases, object features vary, resulting in making it difficult to classify and detect the location of multiple objects. To address this issue, a mmWave-YOLO (you only look once) architecture is proposed that enables highly accurate object classification and location recognition by applying different detectors to each distance data. To provide precise labels for the radar data semi-automatically, a camera-radar cooperative data annotator is also developed. By using the radar only, a real-time (46.6 ms) object classification and location detection of six class objects is achieved. The accuracy of our system is 84 % (mean average precision (mAP)), which is slightly higher than that of the RGB-camera based system (78 %). In addition, since the radar can acquire similar data of target objects against any background, mmWave-YOLO can detect objects in a variety of scenes with only a small variation of the training dataset, resulting in a lower training cost. Experiments confirmed that it can detect objects in outdoor scenes even when it is trained only with indoor scene data.
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