{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:16:50Z","timestamp":1766578610453,"version":"3.37.3"},"reference-count":31,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"Shanxi Key Laboratory of Integrated and Intelligent Navigation","award":["SKLIIN-20180101"],"award-info":[{"award-number":["SKLIIN-20180101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,1]]},"abstract":"<jats:p> Traffic sign recognition (TSR) is a key technology of intelligent vehicles, which is based on visual perception for road information. In view of the fact that the traditional computer vision identification technology cannot meet the requirements of real-time accuracy, the TSR algorithm has been proposed on the basis of improved Lenet-5 algorithm. Firstly, we performed picture noise elimination and image enhancement on selected traffic sign images. Secondly, we used Gabor filter kernel in the convolution layer for convolution operation. In the convolution process, we added normalization layer Batch Normality (BN) after each convolution layer and reduced the data dimension. In the down-sampling layer, we replaced Sigmoid with the Relu activator. Finally, we selected the expanded GTSRB traffic sign database for the comparison experiment on the Caff platform. The experimental results showed that the proposed improved Lenet-5 network test set had the recognition accuracy of 96%, which was better than the method that combined Gabor with Support Vector Machine (SVM) in terms of recognition accuracy and real-time performance. <\/jats:p>","DOI":"10.1142\/s0218001420550034","type":"journal-article","created":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T03:42:32Z","timestamp":1553830952000},"page":"2055003","source":"Crossref","is-referenced-by-count":34,"title":["Study on Traffic Sign Recognition by Optimized Lenet-5 Algorithm"],"prefix":"10.1142","volume":"34","author":[{"given":"Chuanwei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, P.\u00a0R.\u00a0China"},{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710054, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3395-9659","authenticated-orcid":false,"given":"Xiangyang","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niuniu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yupeng","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,6,12]]},"reference":[{"key":"S0218001420550034BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.01.005"},{"key":"S0218001420550034BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749961"},{"issue":"4","key":"S0218001420550034BIB003","first-page":"20","volume":"6","author":"Bowen H.","year":"2016","journal-title":"Intell. 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