The input of external road information is the root of decision-making and control of automatic or auxiliary driving system, and ensuring the quality of input information is the premise of normal operation of the system. This paper mainly studies the application of intelligent vehicle lane recognition based on neural network. Based on the open source traffic scene dataset and the relevant theoretical basis of convolutional neural network, this paper carried out research on lane detection methods, constructed a variety of traffic scene detection models, optimized the model structure, and finally performed well on the experimental test set. Aiming at the problem of lane detection, this paper develops lane detection based on full convolutional network. By reclassifying and labeling the unclear lane lines in the sample images, a data set of lane line detection was constructed. The full convolutional network model is built and the network structure is adjusted to improve the accuracy of detection.

Wenchen Liu and Guozhen Xu. Intelligent Vehicle Lane Recognition Based on Neural Network. International Journal of Neural Network (2021), Vol. 2, Issue 2: 17-24. https://doi.org/10.38007/NN.2021.020203.

[1] Padmajadevi G, Aprameya K S. Offline Signature Verification & Recognition Using Angle Based Feature Extraction & Neural Network Classifier. International Journal of Electronics Engineering Research. (2017) 9(5):711-725.

[2] Noubigh Z, Mezghani A, Kherallah M. Densely Connected Layer to Improve Vggnet-Based CRNN for Arabic Handwriting Text Line Recognition. International Journal of Hybrid Intelligent Systems. (2021) (3):1-15. https://doi.org/10.3233/HIS-210009

[3] Tlemsani R, Benbakreti S, Benyettou A. On-Line Arabic Characters Recognition using Enhanced Time Delay Neural Networks. International Journal on Communications Antenna and Propagation. (2017) Vol 7(4):254-264. https://doi.org/10.15866/irecap.v7i4.13204

[4] Bonilla Cardona D A, Nedjah N, Mourelle L M. Online Phoneme Recognition using Multi-layer Perceptron Networks Combined with Recurrent Non-linear Autoregressive Neural Networks with Exogenous Inputs. Neurocomputing. (2017) 265(nov.22):78-90. https://doi.org/10.1016/j.neucom.2016.09.140

[5] Al-Jubouri M, Abusaimeh H. Offline Arabic Handwritten Isolated Character Recognition System Using Support Vector Machine And Neural Network. Journal of Theoretical and Applied Information Technology. (2017) 95(10):2315-2322.

[6] Mohammed D A, Mezher A, Hadi H S. Off-Line Handwritten Character Recognition Using An Integrated DBSCAN-ANN Scheme. Indonesian Journal of Electrical Engineering and Computer Science. (2019) 14(3):1443-1451. https://doi.org/10.11591/ijeecs.v14.i3.pp1443-1451

[7] Matija R, Offei A, Wang Q. Object Recognition in Aerial Images Using Convolutional Neural Networks. Journal of Imaging. (2017) 3(2):21. https://doi.org/10.3390/jimaging3020021

[8] Alkhateeb J H, Turani A A, Abuhamdah A, et al. An Effective Deep Learning Approach for Improving Off-Line Arabic Handwritten Character Recognition. International Journal of Computer Systems & Software Engineering. (2021) 6(2):104-112.

[9] Sharma A, Jayagopi D B. towards Efficient Unconstrained Handwriting Recognition Using Dilated Temporal Convolution Network. Expert Systems with Applications. (2021) 164(2):114004. https://doi.org/10.1016/j.eswa.2020.114004

[10] Fannas L Y, Sasi A B. Off-Line-Signature-Recognition-Based-On-Angle-Features-and-GRNN-Neural-Networks. International Journal of Parallel Programming. (2019) 7(10, 2013):229-233.

[11] Rashid N, Alim S A, Hashim N, et al. Receiver Operating Characteristics Measure for the Recognition of Stuttering Dysfluencies using Line Spectral Frequencies. IIUM Engineering Journal. (2017) 18(1):193-200. https://doi.org/10.31436/iiumej.v18i1.578

[12] Togootogtokh E, Shih T K, Kumara W, et al. 3D Finger Tracking and Recognition Image Processing for Real-Time Music Playing with Depth Sensors. Multimedia Tools and Applications. (2017) 77(8):1-16. https://doi.org/10.1007/s11042-017-4784-9

[13] Lima J, JVMD Oliveira, Oliveira R. Distributed Artificial Intelligence for Recognition and Position Validation of Printed Circuit Board. European Academic Research. (2019) 7(9):4673.

[14] Aly W, Alya S, Almotairi S. User-Independent American Sign Language Alphabet Recognition Based on Depth Image and PCANet Features. IEEE Access. (2019) (99):1-1. https://doi.org/10.1109/ACCESS.2019.2938829

[15] Khashman A, Oyedotun O K, Olaniyi E O, et al. In-Line Grading System for Mango Fruits Using GLCM Feature Extraction and Soft-Computing Techniques. International Journal of Applied Pattern Recognition. (2019) 6(1):58. https://doi.org/10.1504/IJAPR.2019.10026052

[16] Antwi-Bekoe E, Zhan Q, Xie X, et al. Insulator Recognition and Fault Detection Using Deep Learning Approach. Journal of Physics: Conference Series. (2020) 1454(1):012011 (9pp). https://doi.org/10.1088/1742-6596/1454/1/012011

[17] Tuan L N, Nguyen K C, Nguyen C T, et al. Recognition of Anomalously Deformed Kana Sequences in Japanese Historical Documents. IEICE Transactions on Information and Systems. (2019) E102. D (8):1554-1564. https://doi.org/10.1587/transinf.2018EDP7361

[18] Shinohara N, Hashimoto M. Automatic Determination of Reference Pixels Using Middle Layer of Deep Neural Network for Template Matching. Journal of the Japan Society for Precision Engineering. (2018) 84(12):1079-1084. https://doi.org/10.2493/jjspe.84.1079