Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …

This paper reviews automated visual-based defect detection approaches applicable to
various materials, such as metals, ceramics and textiles. In the first part of the paper, we …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Vehicle and pedestrian detection is one of the critical tasks in autonomous driving. Since
heterogeneous techniques have been proposed, the selection of a detection system with an …

This paper presents the results of the evaluation of five deep learning architectures for the
classification of soybean pest images. The performance of Inception-v3, Resnet-50, VGG-16 …

Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …

Insect pests are one of the main factors affecting agricultural product yield. Accurate
recognition of insect pests facilitates timely preventive measures to avoid economic losses …

Aerial scene classification, which aims to automatically label an aerial image with a specific
semantic category, is a fundamental problem for understanding high-resolution remote …