Yingjin Wang, Chuanming Wang, Yuchao Zheng, Huiyuan Fu, Huadong Ma
{"title":"基于变压器的车辆颜色细粒度分类神经网络","authors":"Yingjin Wang, Chuanming Wang, Yuchao Zheng, Huiyuan Fu, Huadong Ma","doi":"10.1109/MIPR51284.2021.00025","DOIUrl":null,"url":null,"abstract":"The development of vehicle color recognition technology is of great significance for vehicle identification and the development of the intelligent transportation system. However, the small variety of colors and the influence of the illumination in the environment make fine-grained vehicle color recognition a challenge task. Insufficient training data and small color categories in previous datasets causes the low recognition accuracy and the inflexibility of practical using. Meanwhile, the inefficient feature learning also leads to poor recognition performance of the previous methods. Therefore, we collect a rear shooting dataset from vehicle bayonet monitoring for fine-grained vehicle color recognition. Its images can be divided into 11 main-categories and 75 color subcategories according to the proposed labeling algorithm which can eliminate the influence of illumination and assign the color annotation for each image. We propose a novel recognition model which can effectively identify the vehicle colors. We skillfully interpolate the Transformer into recognition model to enhance the feature learning capacity of conventional neural networks, and specially design a hierarchical loss function through in-depth analysis of the proposed dataset. We evaluate the designed recognition model on the dataset and it can achieve accuracy of 97.77%, which is superior to the traditional approaches.","PeriodicalId":139543,"journal":{"name":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transformer based Neural Network for Fine-Grained Classification of Vehicle Color\",\"authors\":\"Yingjin Wang, Chuanming Wang, Yuchao Zheng, Huiyuan Fu, Huadong Ma\",\"doi\":\"10.1109/MIPR51284.2021.00025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of vehicle color recognition technology is of great significance for vehicle identification and the development of the intelligent transportation system. However, the small variety of colors and the influence of the illumination in the environment make fine-grained vehicle color recognition a challenge task. Insufficient training data and small color categories in previous datasets causes the low recognition accuracy and the inflexibility of practical using. Meanwhile, the inefficient feature learning also leads to poor recognition performance of the previous methods. Therefore, we collect a rear shooting dataset from vehicle bayonet monitoring for fine-grained vehicle color recognition. Its images can be divided into 11 main-categories and 75 color subcategories according to the proposed labeling algorithm which can eliminate the influence of illumination and assign the color annotation for each image. We propose a novel recognition model which can effectively identify the vehicle colors. We skillfully interpolate the Transformer into recognition model to enhance the feature learning capacity of conventional neural networks, and specially design a hierarchical loss function through in-depth analysis of the proposed dataset. We evaluate the designed recognition model on the dataset and it can achieve accuracy of 97.77%, which is superior to the traditional approaches.\",\"PeriodicalId\":139543,\"journal\":{\"name\":\"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MIPR51284.2021.00025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MIPR51284.2021.00025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Transformer based Neural Network for Fine-Grained Classification of Vehicle Color
The development of vehicle color recognition technology is of great significance for vehicle identification and the development of the intelligent transportation system. However, the small variety of colors and the influence of the illumination in the environment make fine-grained vehicle color recognition a challenge task. Insufficient training data and small color categories in previous datasets causes the low recognition accuracy and the inflexibility of practical using. Meanwhile, the inefficient feature learning also leads to poor recognition performance of the previous methods. Therefore, we collect a rear shooting dataset from vehicle bayonet monitoring for fine-grained vehicle color recognition. Its images can be divided into 11 main-categories and 75 color subcategories according to the proposed labeling algorithm which can eliminate the influence of illumination and assign the color annotation for each image. We propose a novel recognition model which can effectively identify the vehicle colors. We skillfully interpolate the Transformer into recognition model to enhance the feature learning capacity of conventional neural networks, and specially design a hierarchical loss function through in-depth analysis of the proposed dataset. We evaluate the designed recognition model on the dataset and it can achieve accuracy of 97.77%, which is superior to the traditional approaches.