{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:36:49Z","timestamp":1778168209285,"version":"3.51.4"},"reference-count":18,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52362050"],"award-info":[{"award-number":["52362050"]}]},{"name":"National Natural Science Foundation of China","award":["300102223505"],"award-info":[{"award-number":["300102223505"]}]},{"name":"\u201cHongliu Excellent Young\u201d Talents Support Program of Lanzhou University of Technology","award":["52362050"],"award-info":[{"award-number":["52362050"]}]},{"name":"\u201cHongliu Excellent Young\u201d Talents Support Program of Lanzhou University of Technology","award":["300102223505"],"award-info":[{"award-number":["300102223505"]}]},{"name":"Fundamental Research Funds for the Central Universities, Chang\u2019an University","award":["52362050"],"award-info":[{"award-number":["52362050"]}]},{"name":"Fundamental Research Funds for the Central Universities, Chang\u2019an University","award":["300102223505"],"award-info":[{"award-number":["300102223505"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle type and brand information constitute a crucial element in intelligent transportation systems (ITSs). While numerous appearance-based classification methods have studied frontal view images of vehicles, the challenge of multi-pose and multi-angle vehicle distribution has largely been overlooked. This paper proposes an appearance-based classification approach for multi-angle vehicle information recognition, addressing the aforementioned issues. By utilizing faster regional convolution neural networks, this method automatically captures crucial features for vehicle type and brand identification, departing from traditional handcrafted feature extraction techniques. To extract rich and discriminative vehicle information, ZFNet and VGG16 are employed. Vehicle feature maps are then imported into the region proposal network and classification location refinement network, with the former generating candidate regions potentially containing vehicle targets on the feature map. Subsequently, the latter network refines vehicle locations and classifies vehicle types. Additionally, a comprehensive vehicle dataset, Car5_48, is constructed to evaluate the performance of the proposed method, encompassing multi-angle images across five vehicle types and 48 vehicle brands. The experimental results on this public dataset demonstrate the effectiveness of the proposed approach in accurately classifying vehicle types and brands.<\/jats:p>","DOI":"10.3390\/s23239569","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T13:45:49Z","timestamp":1701524749000},"page":"9569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Hongying","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}]},{"given":"Xusheng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}]},{"given":"Huazhi","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"Key Laboratory of Transportation Industry of Automotive Transportation Safety Enhancement Technology, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Huagang","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Yaru","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Siyan","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electronic and Control Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.cviu.2008.08.006","article-title":"Object tracking using SIFT features and mean shift","volume":"113","author":"Zhou","year":"2009","journal-title":"Comput. 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