Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding

Abstract

Recent advances in large-scale image and video analysis have empowered the potential capabilities of visual surveillance systems. In particular, deep learning-based approaches bring in substantial benefits in solving certain computer vision problems such as fine-grained object recognition. Here, the authors mainly concentrate on classification and identification of maritime vessels and land vehicles, which are the key constituents of visual surveillance systems. Employing publicly available data sets for maritime vessels and land vehicles, the authors aim to improve visual recognition. Specifically, the authors focus on five tasks regarding visual recognition; coarse-grained classification, fine-grained classification, coarse-grained retrieval, fine-grained retrieval, and verification. To increase the performance in these tasks, the authors utilise a multi-task learning framework and present a novel loss function which simultaneously considers deep feature learning and classification by exploiting the available hierarchical labels of individual samples and the global statistics of distances between the data pairs. The authors observe that the proposed multi-task learning model improves the fine-grained recognition performance on MARVEL and Stanford Cars data sets, compared to training of a model targeting a single recognition task.

Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

“…Another important application is in the context of the visual surveillance system. Solmaz et al (2018) found that deep learning-based CNN models improve the accuracy of visual recognition and verification of maritime vessels and land vehicles. Taking the CNN one step further, Gallego et al (2019) proposed the Convolutional Long Short Term Memory Selectional AutoEncoders (CMSAE) neural architecture to predict maritime oil spills using Side-Looking Airborne Radar (SLAR) images.…”

Deep Convolutional Feature Fusion Model for Multispectral Maritime Imagery Ship Recognition

“…3) DyFusion [42], 4) SF-SRDA [43], and with three methods for VIS images in the paired images: 5) MFL (feature-level) + ELM [38], 6) CNN + Gabor + MS-CLBP [36], 7) ME-CNN [40], and with one method for all time IR images: 8) ELM-CNN [31]. Table 5 shows the comparison results using the mean pre-class recognition accuracy as evaluation measure.…”

Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

“…Shin et al [22] proposed a model using interest region combined a convolutional neural network for improving the ship images' classification accuracy. Solmaz et al [23] proposed a framework and a new loss function to recognize the marine and land vehicles in a fine-grained way using multitasking learning.…”