Vehicle detection in foggy weather combining millimeter wave rada and machine vision
Image Processing|更新时间:2023-10-10
|
Vehicle detection in foggy weather combining millimeter wave rada and machine vision
Chinese Journal of Liquid Crystals and DisplaysVol. 38, Issue 10, Pages: 1445-1454(2023)
作者机构:
1.陕西科技大学 电子信息与人工智能学院, 陕西 西安 710021
作者简介:
基金信息:
Project of Science and Technology Department of Shaanxi Province(S2023-YF-YBNY-0232);Xi'an Science and Technology Plan(22NYYF064);Xi'an Weiyang District Science and Technology Plan(201305)
LI Qi, YE Xiao-min, FENG Wen-bin. Vehicle detection in foggy weather combining millimeter wave rada and machine vision. [J]. Chinese Journal of Liquid Crystals and Displays 38(10):1445-1454(2023)
DOI:
LI Qi, YE Xiao-min, FENG Wen-bin. Vehicle detection in foggy weather combining millimeter wave rada and machine vision. [J]. Chinese Journal of Liquid Crystals and Displays 38(10):1445-1454(2023) DOI: 10.37188/CJLCD.2022-0412.
Vehicle detection in foggy weather combining millimeter wave rada and machine vision
Vehicle detection is very vital to the assisted driving system. Due to the serious degradation of the foggy road scene, the vehicle information in the image is not obvious, resulting in missed detection and false detection problems in vehicle detection. Aiming at the above problems, a vehicle detection method in foggy weather combining millimeter-wave radar and machine vision is proposed. First, the dark channel dehazing algorithm is used to preprocess the image to improve the salience of vehicle information in the image under foggy conditions. Then, the knowledge distillation is used to improve the YOLOv5s algorithm, and the knowledge distillation is introduced into the feature extraction network of YOLOv5s, which is used in the target positioning and classification stages to calculate the distillation loss and backpropagate the loss to train a small network model to improve the detection speed while ensuring the accuracy of visual detection. Finally, the distance matching algorithm based on the search of potential target detection areas is used to compare the visual detection results and the millimeter-wave radar detection results decision-making fusion. Based on the type and distance of the detected target, the interference information and erroneous information is filtered out, and the targets with high confidence after fusion in millimeter-wave radar detection and visual detection is retained. Thereby, the accuracy of vehicle detection is improved. The experimental results show that the method has the highest detection accuracy rate of 92.8% and the recall rate of 90.7% in foggy weather, which can realize the detection of vehicles in foggy weather.
GUO Y, LIANG R L, CUI Y K, et al. A domain-adaptive method with cycle perceptual consistency adversarial networks for vehicle target detection in foggy weather [J]. IET Intelligent Transport Systems, 2022, 16(7): 971-981. doi: 10.1049/itr2.12190http://dx.doi.org/10.1049/itr2.12190
HE K M, SUN J, TANG X O, et al. Single image haze removal using dark channel prior [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353. doi: 10.1109/tpami.2010.168http://dx.doi.org/10.1109/tpami.2010.168
GAO T, LI K, CHEN T, et al. A novel UAV sensing image defogging method [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 2610-2625. doi: 10.1109/jstars.2020.2998517http://dx.doi.org/10.1109/jstars.2020.2998517
ZHANG B L, ZHAN Y H, PAN D W, et al. Vehicle detection based on fusion of millimeter-wave radar and machine vision [J]. Automotive Engineering, 2021, 43(4): 478-484. (in Chinese). doi: 10.19562/j.chinasae.qcgc.2021.04.004http://dx.doi.org/10.19562/j.chinasae.qcgc.2021.04.004
JIANG Q Y, ZHANG L J, MENG D J. Target detection algorithm based on MMW radar and camera fusion [C]//Proceedings of 2019 IEEE Intelligent Transportation Systems Conference. Auckland: IEEE, 2019: 1-6. doi: 10.1109/itsc.2019.8917504http://dx.doi.org/10.1109/itsc.2019.8917504
HAN S Y, WANG X, XU L H, et al. Frontal object perception for intelligent vehicles based on radar and camera fusion [C]//Proceedings of the 35th Chinese Control Conference. Chengdu: IEEE, 2016: 4003-4008. doi: 10.1109/chicc.2016.7553978http://dx.doi.org/10.1109/chicc.2016.7553978
LUO Y, ZHAO Y F, LI J X, et al. Computational imaging without a computer: seeing through random diffusers at the speed of light [J]. eLight, 2022, 2(1): 4. doi: 10.1186/s43593-022-00012-4http://dx.doi.org/10.1186/s43593-022-00012-4
ZUO C, QIAN J M, FENG S J, et al. Deep learning in optical metrology: a review [J]. Light: Science & Applications, 2022, 11(1): 39. doi: 10.1038/s41377-022-00714-xhttp://dx.doi.org/10.1038/s41377-022-00714-x
Guohai SITU G. Deep holography [J]. Light: Advanced Manufacturing, 2022, 3(2): 278-300. doi: 10.37188/lam.2022.013http://dx.doi.org/10.37188/lam.2022.013
LIU H Y, SUN F Q, GU J, et al. SF-YOLOv5: a lightweight small object detection algorithm based on improved feature fusion mode [J]. Sensors, 2022, 22(15): 5817. doi: 10.3390/s22155817http://dx.doi.org/10.3390/s22155817
LIU Y F, JI H J, LIU L B. Real-time detection model of highway vehicle based on YOLOv5s [J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(9): 1228-1241. (in Chinese). doi: 10.37188/CJLCD.2022-0026http://dx.doi.org/10.37188/CJLCD.2022-0026
DAI G W, FAN J C. An industrial-grade solution for crop disease image detection tasks [J]. Frontiers in Plant Science, 2022, 13: 921057. doi: 10.3389/fpls.2022.921057http://dx.doi.org/10.3389/fpls.2022.921057
XU D P, LIU Y Q, WANG Q, et al. Target detection based on improved hausdorff distance matching algorithm for millimeter-wave radar and video fusion [J]. Sensors, 2022, 22(12): 4562. doi: 10.3390/s22124562http://dx.doi.org/10.3390/s22124562
CHEN B, PEI X F, CHEN Z F. Research on target detection based on distributed track fusion for intelligent vehicles [J]. Sensors, 2020, 20(1): 56. doi: 10.3390/s20010056http://dx.doi.org/10.3390/s20010056