Recognition of Truck Brands Using Fused Deep Neural Networks Based on Transfer Learning

Abstract

Long-distance truck transportation often results in stained, faded, or blurry frontal images, leading to fluctuations in image resolution and feature degradation. To enable high-precision truck brand recognition in challenging highway scenarios, this study evaluates convolutional neural networks (CNNs) enhanced by transfer learning (TL). First, four typical neural networks, such as InceptionV3 based on TL, Xception based on TL, Xception based on TL and DenseNet201 based on TL, are exploited for the recognition of truck brands. Second, a new network architecture is proposed, a fused deep neural network (FDNN), based on transfer learning for the recognition of truck brands (FDNN–TL–RTB), which integrates the convolution features of the last layer of InceptionV3–TL–RTB, Xception–TL–RTB, and DenseNet-201–TL–RTB networks based on the tandem fusion rules. Finally, the comparative experiments on CNNs are carried out using the Truck Brands Data Sets of Southeast University. The data set was obtained using continuous capture with cameras installed on the highway to obtain data under various conditions. The experimental results demonstrate that the proposed FDNN–TL–RTB network achieves a superior recognition accuracy of 98.16% on the test set. Although Shidai vehicles presented the most significant classification challenges among the 23 truck brand categories, the FDNN–TL–RTB method achieved a 95% recognition accuracy for this category. The high accuracy and robustness of the model highlight its significant potential for practical applications in intelligent transportation systems, such as automated toll collection and traffic flow monitoring.