@article{ref1,
title="MME-YOLO: multi-sensor multi-level enhanced YOLO for robust vehicle detection in traffic surveillance",
journal="Sensors (Basel)",
year="2021",
author="Zhu, Jianxiao and Li, Xu and Jin, Peng and Xu, Qimin and Sun, Zhengliang and Song, Xiang",
volume="21",
number="1",
pages="e27-e27",
abstract="As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To  improve the vehicle detection abilities in such online safety systems, in this  paper, we propose a novel multi-sensor multi-level enhanced convolutional network  model, called multi-sensor multi-level enhanced convolutional network architecture  (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination,  and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the  enhanced inference head and the LiDAR-Image composite module. More specifically, the  enhanced inference head preliminarily equips the network with stronger inference  abilities for redundant visual cues by attention-guided feature selection blocks and  anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite  module cascades the multi-level feature maps from the LiDAR subnet to the image  subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP  improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the  composite module, the overall architecture gains 91.63% mAP in the collected  Road-side Dataset. Experiments show that even under the abnormal lightings and the  inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable  recognition accuracy and robust detection performance.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010027",
url="http://dx.doi.org/10.3390/s21010027"
}