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However, the images taken in ultra-micro light environments usually have inherent defects such as extremely low brightness and contrast, a high noise level, and serious loss of scene details and colors, which leads to great challenges in the research of low-light image and object detection and classification. The low-light night vision image used as the study object in this work has an excessively dim overall picture and very little information about the screen\u2019s features. Three algorithms, HE, AHE, and CLAHE, were used to enhance and highlight the image. The effectiveness of these image enhancement methods is evaluated using metrics such as the peak signal-to-noise ratio and mean square error, and CLAHE was selected after comparison. The target image includes vehicles, people, license plates, and objects. The gray-level co-occurrence matrix (GLCM) was used to extract the texture features of the enhanced images, and the extracted image texture features were used as input to construct a backpropagation (BP) neural network classification model. Then, low-light image classification models were developed based on VGG16 and ResNet50 convolutional neural networks combined with low-light image enhancement algorithms. The experimental results show that the overall classification accuracy of the VGG16 convolutional neural network model is 92.1%. Compared with the BP and ResNet50 neural network models, the classification accuracy was increased by 4.5% and 2.3%, respectively, demonstrating its effectiveness in classifying low-light night vision targets.<\/jats:p>","DOI":"10.3390\/e26100882","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T10:49:22Z","timestamp":1729507762000},"page":"882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Research on Target Image Classification in Low-Light Night Vision"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6015-1671","authenticated-orcid":false,"given":"Yanfeng","family":"Li","sequence":"first","affiliation":[{"name":"School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510632, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongbiao","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510632, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjian","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510632, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2313-1893","authenticated-orcid":false,"given":"Guiqian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing, Guangzhou Panyu Polytechnic, Guangzhou 511483, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiekai","family":"Gong","sequence":"additional","affiliation":[{"name":"Guangdong Railway Planning and Design Institute Co., Guangzhou 510600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12415","DOI":"10.1109\/ACCESS.2019.2892425","article-title":"Fast pedestrian detection in surveillance video based on soft target training of shallow random forest","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Oluyide, O.M., Tapamo, J., and Walingo, T.M. 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