BinVPR: Binary Neural Networks towards Real-Valued for Visual Place Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 13; p. 4130
• Main Authors: Wang, Junshuai, Han, Junyu, Dong, Ruifang, Kan, Jiangming
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.06.2024
• Subjects: Accuracy; binary neural networks; Computer vision; Datasets; Deep learning; Efficiency; Energy consumption; gradient mismatch; gradient vanishing; Hypotheses; Image retrieval; Investigations; Localization; model compression; Neural networks; Principles; Robots; visual place recognition; model compression; binary neural networks; gradient mismatch; gradient vanishing; visual place recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24134130

Visual Place Recognition (VPR) aims to determine whether a robot or visual navigation system locates in a previously visited place using visual information. It is an essential technology and challenging problem in computer vision and robotic communities. Recently, numerous works have demonstrated that the performance of Convolutional Neural Network (CNN)-based VPR is superior to that of traditional methods. However, with a huge number of parameters, large memory storage is necessary for these CNN models. It is a great challenge for mobile robot platforms equipped with limited resources. Fortunately, Binary Neural Networks (BNNs) can reduce memory consumption by converting weights and activation values from 32-bit into 1-bit. But current BNNs always suffer from gradients vanishing and a marked drop in accuracy. Therefore, this work proposed a BinVPR model to handle this issue. The solution is twofold. Firstly, a feature restoration strategy was explored to add features into the latter convolutional layers to further solve the gradient-vanishing problem during the training process. Moreover, we identified two principles to address gradient vanishing: restoring basic features and restoring basic features from higher to lower layers. Secondly, considering the marked drop in accuracy results from gradient mismatch during backpropagation, this work optimized the combination of binarized activation and binarized weight functions in the Larq framework, and the best combination was obtained. The performance of BinVPR was validated on public datasets. The experimental results show that it outperforms state-of-the-art BNN-based approaches and full-precision networks of AlexNet and ResNet in terms of both recognition accuracy and model size. It is worth mentioning that BinVPR achieves the same accuracy with only 1% and 4.6% model sizes of AlexNet and ResNet.