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http://dx.doi.org/10.6109/jkiice.2022.26.9.1330

Lightweight Deep Learning Model for Real-Time 3D Object Detection in Point Clouds  

Kim, Gyu-Min (School of Electronics and Information Engineering, Korea Aerospace University)
Baek, Joong-Hwan (School of Electronics and Information Engineering, Korea Aerospace University)
Kim, Hee Yeong (LinktoTo Co. Ltd)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.9, 2022 , pp. 1330-1339 More about this Journal
Abstract
3D object detection generally aims to detect relatively large data such as automobiles, buses, persons, furniture, etc, so it is vulnerable to small object detection. In addition, in an environment with limited resources such as embedded devices, it is difficult to apply the model because of the huge amount of computation. In this paper, the accuracy of small object detection was improved by focusing on local features using only one layer, and the inference speed was improved through the proposed knowledge distillation method from large pre-trained network to small network and adaptive quantization method according to the parameter size. The proposed model was evaluated using SUN RGB-D Val and self-made apple tree data set. Finally, it achieved the accuracy performance of 62.04% at mAP@0.25 and 47.1% at mAP@0.5, and the inference speed was 120.5 scenes per sec, showing a fast real-time processing speed.
Keywords
3D Object Detection; Point Cloud; Deep Learning; Knowledge Distillation; Quantization;
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Times Cited By KSCI : 2  (Citation Analysis)
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