Seagull Optimization With Deep Learning Driven Condition Invariant Visual Place Recognition Model

Abstract

Visual place recognition (VPR) is most important topic in the computer vision (CV) and robotics community, whereas the task is for efficiently and accurately recognize the place of a provided query image. VPR still remains an open problem because of the several manners whereas the presence of real-world locations can change. As condition-invariant and viewpoint-invariant features were important features to long-term robust visual place-detection, the accomplishment of deep learning (DL) approaches from the CV domain triggered a kind of primary studies as its efficacy for VPR utilizing generic features in networks which are trained for another kind of detection tasks. In this aspect, this article develops a Seagull Optimization with Deep Learning Driven Condition Invariant Visual Place Recognition (SGODL-CIVPR) model. The presented SGODL-CIVPR model majorly involves the recognition of live places with respect to reference images. To accomplish this, the SGODL-CIVPR technique utilizes deep convolutional neural network based EfficientNet model to produce features, and the hyperparameter tuning process takes place using the SGO algorithm. Besides, convolutional wavelet neural network (CWNN) with deep convolutional generative adversarial network (DCGAN) is used to map features to low dimensional space for improving invariant properties and decrease dimensions concurrently. The performance validation of the SGODL-CIVPR model is tested using different datasets and the results demonstrate better performance over other methods.

Evaluation of Global Descriptor Methods for Appearance-Based Visual Place Recognition

“…Although Recall@N has been widely used in image retrieval tasks, that is, the correct retrieval only needs to be among the Top-N candidates, the allowable range for VPR or loop closure detection is more stringent from the viewpoint of practical application. In addition, the motivation behind [44] [14, 17, 40] ResNet [45] [45, 62] ShufeNet [57,63] [64] MobileNet [55,56] [65-68] EfcientNet [58] [ 69,70] Customized CNN-based CALC [46] [46] NetVLAD [14] [14, 70-75] MobileNetVLAD [76] [76, 77] DBoW3-SuperPoint [78] [79] Autoencoder [80] [16, 46, 81] Variational autoencoder [82] [83]…”