Road Visibility Estimation Method Based on AlexNet Algorithm

doi:10.3969/j.issn.1006-2475.2019.06.015

Abstract

Abstract: In this paper, AlexNet neural network algorithm is used to construct a framework of highway visibility recognition. Through the collection of road camera images, the images are labeled, the AlexNet algorithm is trained, image visibility characteristics are extracted, the visibility recognition model is constructed, and the road camera image is accessed in real time to realize the estimation of visibility values. The visibility recognition results are analyzed on 150000 samples labeled with visibility value extracted from 42 surveillance cameras in Anhui province. The results show that the average recognition rate of 42 points is 78.02%. Among them, 14 sites have more than 90% recognition rate and 21 sites have more than 80% recognition rate. The road visibility estimation method based on AlexNet algorithm satisfies the requirements of road visibility real-time and recognition accuracy, and can be used as an auxiliary visibility monitoring method in areas where the visibility meter is not installed. Meanwhile, it has good robustness to illumination changes, distance, and so on.

Key words: AlexNet algorithm, image recognition, CNN, visibility

CLC Number:

TP301.6

MIAO Kai-chao1,2, WANG Chuan-hui2, ZHANG Ya-li2, ZHOU Jian-ping2, LIU Cheng-xiao2, YAO Ye-qing2. Road Visibility Estimation Method Based on AlexNet Algorithm[J]. Computer and Modernization, 2019, 0(06): 87-.

References

［1］丁一汇,柳艳菊. 近50年我国雾和霾的长期变化特征及其与大气湿度的关系［J］. 中国科学:地球科学, 2014,44(1):37-48.
［2］潘本锋,汪巍,李亮,等. 我国大中型城市秋冬季节雾霾天气污染特征与成因分析［J］. 环境与可持续发展, 2013,38(1):33-36.
［3］中华人民共和国国家统计局. 中华人民共和国2017年国民经济和社会发展统计公报［N］. 人民日报, 2018-02-28.
［4］宋洪军,郜园园,陈阳舟. 基于摄像机动态标定的交通能见度估计［J］. 计算机学报, 2015,38(6):1172-1187.
［5］夏冬,吴志权,谭浩波,等. 广东省能见度自动观测系统资料评估分析与订正［J］. 气象科技, 2014,42(1):68-72.
［6］唐延婧,裴兴云. 贵州交通站资料应用于山区高速公路低能见度研究［J］. 热带气象学报, 2015,31(2):280-288.
［7］丁秋冀,包云轩,袁成松,等. 沪宁高速公路团雾发生规律及局地性分析［J］. 气象科学, 2013,33(6):634-642.
［8］包云轩,丁秋冀,袁成松,等. 沪宁高速公路一次复杂性大雾过程的数值模拟试验［J］. 大气科学, 2013,37(1):124-136.
［9］田小毅,吴建军,严明良,等. 高速公路低能见度浓雾监测预报中的几点新进展［J］. 气象科学, 2009,29(3):414-420.
［10］BABARI R, HAUTIERE N, DUMONT E, et al. Visibility monitoring using conventional roadside cameras［J］. Transportation Research Part C: Emerging Technologies, 2012,22:17-28.
［11］郭尚书,齐文新,齐宇. 基于暗通道先验的视频能见度测量方法［J］. 计算机与数字工程, 2014,42(6):694-697.
［12］赵亚. 基于深度学习的能见度预测系统的研究［D］. 石家庄:河北科技大学, 2017.
［13］吕宝磊,胡泳涛,李林,等. 利用集合深度学习方法融合多源数据开发全国能见度网格数据［J］. 气象科技进展, 2018,8(6):77-82.
［14］〖ZK(#〗朱敏. 基于深度学习的雾检测方法［J］. 科学技术创新, 2018(36):6-7.
［15］DENG L. Deep learning: From speech recognition to language and multimodal processing［J］. APSIPA Transactions on Signal and Information Processing, 2016,5:1-15.
［16］WU M Y, CHEN L. Image recognition based on deep learning［C］// 2015 Chinese Automation Congress. 2015:542-546.
［17］KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［C］// Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012:1097-1105.
［18］CIREGAN D, MEIER U, SCHMIDHUBERJ. Multi-column deep neural networks for image classification［J］. Computer Science, 2012,157(10):3642-3649.
［19］ERHAN D, SZEGEDY C, TOSHEV A, et al. Scalable object detection using deep neural networks［C］// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014:2147-2154.
［20］HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014,37(9):1904-1916.
［21］CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs［J］. Computer Science, 2014(4):357-361.
［22］KARPATHY A, LI F F. Deep visual-semantic alignments for generating image descriptions［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016,39(4):664-676.

[1]	HUANG Shanshan1, WU Wei2, XU Yuqing1, WEI Jie1. Pipelines in Drawings Detection Method Based on Improved Mask R-CNN and LSD [J]. Computer and Modernization, 2024, 0(10): 42-48.
[2]	ZHANG Gao-yi1, XU Yang1, 2, CAO Bin1, 2, SHI Jin1. Global Cross-layer Interaction Networks Learning Fine-grained Images Features Representation [J]. Computer and Modernization, 2024, 0(03): 97-104.
[3]	LI Shi-da, XIANG Jian-wen. A Weakened Joint Reinforcement Method to Improve Robustness of Image Recognition Models [J]. Computer and Modernization, 2023, 0(10): 70-76.
[4]	WU Tian, LIU Hai-hua, TONG Shun-yan. Image Classification Based on Deep Feedback CNN [J]. Computer and Modernization, 2023, 0(09): 82-86.
[5]	LI Shi-qiu. A Collaborative Representation-based Method of Discriminant Locality Preserving Projections [J]. Computer and Modernization, 2023, 0(06): 43-47.
[6]	ZHU Li-qing, LI Xiang, . Vehicle Detection of Remote Sensing Images Based on Improved YOLOv5 Algorithm [J]. Computer and Modernization, 2023, 0(05): 117-121.
[7]	WANG Yan, YANG Feng-wei, ZHAI Xing, WANG Li, TANG Yan, LIU Zhe, HAN Ai-qing. Computer Aided Diagnostic on Microscopic Images Based on Deep Learning [J]. Computer and Modernization, 2023, 0(03): 54-59.
[8]	SHI Zhan-kun, YANG Feng, HAN Jian-ning, GUO Xin, CAO Shang-bin. Apples Recognition in Natural Environment Based on Faster-RCNN [J]. Computer and Modernization, 2023, 0(02): 62-65.
[9]	WANG Chang-jun, PENG Cheng, LI Yong. Multi-feature Fusion Ship Target Detection Algorithm in Complex Environment [J]. Computer and Modernization, 2022, 0(11): 81-88.
[10]	YIN Chu, ZHAO Qi-lin, RUI Ting, YUAN Hui, WANG Jian. Crack Target Detection Algorithm Based on Adaptive Anchor Frame [J]. Computer and Modernization, 2022, 0(11): 95-101.
[11]	LIN Hang, GENG Duo-fei, YU Hao, HU Dan, ZHANG Ke. An Insulator Self-detonation Detection Algorithm on Transmission Line Based on Double Modules [J]. Computer and Modernization, 2022, 0(07): 15-20.
[12]	XU Ya-jun, GUO En-hao, CHEN Lin, SI Cheng-ke. A Data-driven Deep Modulation Identification Method for RF Signals [J]. Computer and Modernization, 2022, 0(06): 80-86.
[13]	LIU Xue-hu, OU Ou, ZHANG Wei-jing, DU Xue-lei. Landslide Image Detection Based on Dilated Convolution and Attention Mechanism [J]. Computer and Modernization, 2022, 0(04): 45-51.
[14]	WU Biao, ZHOU Qing-hua, ZENG Xiao-wei. Stencil Character Recognition of Paper Medicine Packaging Based on Mask-RCNN [J]. Computer and Modernization, 2022, 0(02): 108-113.
[15]	XU Jian, ZHANG Hao, XU Hang, XIE Kai. Detecting Electrical Circuit Elements Based on Faster RCNN [J]. Computer and Modernization, 2021, 0(12): 53-57.