Object Detection for Aircraft Turnover Milestone based on Modified
DOI:
https://doi.org/10.54097/fcis.v5i3.13847Keywords:
Object Detection, Haze Environment, Aircraft Turnaround Milestone, YOLOv7, Multiple Detection Head, Attention MechanismAbstract
In the target detection task of aircraft turnaround milestone in foggy scenario, there are some problems such as unstable location of prediction frame boundary, high error detection rate and poor detection effect of small target. A new target detection method BTM-YOLO (Broad-sighted upsample and three-dimensional attention multiple detection head YOLO) is proposed, which is based on YOLOv7 network. Add a small target detection head to improve the ability of small target detection; The up-sampling module OVRAFE is introduced to reduce the information loss in the up-sampling process. Replace CIoU with Median Wise IoU (MWIoU) to suppress the problem of poor sample swelling in data sets. The improved model makes up for the performance shortcomings of small target detection in foggy days, and the average detection accuracy on the real foggy day test set is 76.2%, which is 3.32% higher than that of the original model, basically meeting the task requirements.
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References
GE Z, LIU S , WANG F ,et al.YOLOX: Exceeding YOLO Series in 2021[J]. 2021.DOI:10.48550/arXiv.2107.08430.
WANG C Y, BOCHKOVSKIY A , LIAO H Y M .YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J].arXiv e-prints, 2022.DOI: 10.48550/ arXiv. 2207. 02696.
REN S, HE K, GIRSHICK R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.
Huang G W, Li W, Zhang B H, et al. Improved SSD-Based Multi-scale Object Detection Algorithm in Airport Surface[J]. Computer Engineering and Applications, 2022,58(05):264-270.
Wang Y, Yuan G W, Qu R, et al. Target Detection Method of Airport Apron Based on Improved YOLOv3[J].Journal of Zhengzhou University (Natural Science Edition), 2022,54 (05): 22-28.DOI:10.13705/j.issn.1671-6841.2021287.
Xia Z H,Wei R X,Tu J,et al. Moving Target Detection Method for General Aviation Airport[J]. Science Technology and Engineering, 2022,22(29):13114-13119.
Yi J H, Qu S J, Yao Z K,et al. Traffic sign recognition model in haze weather based on YOLOv5[J]. Journal of Computer Applications, ,2022,42(09):2876-2884.
Yin X P, Zhong P, Xue W,et al. The Method of UAV Image Object Detection under Foggy Weather by Style Transfer[J]. Aero Weaponry, ,2021,28(03):22-30.
Liu S G, Zhang L K,Du H D,et al. Improved Object Detection of YOLOv4 in Foggy Conditions[J/OL]. Journal of System Simulation: 1-10[2023-08-07]. DOI: 10. 16182/ j.issn 1004731 x.joss.22-0423.
MA N, ZHANG X, ZHENG H T, et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design[C]// Proceedings of the European conference on computer vision (ECCV). 2018: 116-131.
KUPYN O, MARTYNIUK T, WU J, et al. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better[C]// Proceedings of the IEEE/CVF international conference on computer vision. 2019: 8878-8887.
Yang K Z, Yan X N,Sun J, et al. A DeRf-YOLOv3-X Object Detection Method for Rainy and Foggy Background[J]. Chinese Journal of Sensors and Actuators, 2022,35(09):1222-1229.
CHOLLET F. Xception: Deep learning with depthwise separable convolutions [C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251-1258.
LIU X, MA Y, SHI Z, et al. Griddehazenet: Attention-based multi-scale network for image dehazing[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 7314-7323.
Xie Y H, Xie Y, Chen L,et al. Object Detection in Real-World Hazy Scene[J]. Journal of Computer-Aided Design & Computer Graphics,2021,33(05):733-745.
Lv Z L, Chen L Y. A Novel Deep Neural Network Compression Model for Airport Object Detection[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2021,38 (04): 571-586.DOI:10.16356/j.1005-1120.2021.04.004.
LOY C C , LIN D , WANG J ,et al. CARAFE: Content-Aware ReAssembly of FEatures.2019[2023-08-07].
YANG L, ZHANG R Y, LI L, et al. Simam: A simple, parameter-free attention module for convolutional neural networks [C]//International conference on machine learning. PMLR, 2021: 11863-11874.
ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: Faster and better learning for bounding box regression [C]// Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 12993-13000.
TONG Z, CHEN Y, XU Z, et al. Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism[J]. arXiv preprint arXiv:2301.10051, 2023.
