Novel patch selection based on object detection in HMAX for natural image classification

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

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Dangerous Scenes Recognition During Hoisting Based on Faster Region-Based Convolutional Neural Network

Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies ◽

10.1115/smasis2018-8226 ◽

2018 ◽

Author(s):

Hongguo Su ◽

Mingyuan Zhang ◽

Shengyuan Li ◽

Xuefeng Zhao

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Image Classification ◽

State Of The Art ◽

Spatial Location ◽

Average Precision ◽

Practical Applications ◽

Security Enhancement ◽

Human Interactions

In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

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A Comparison of Techniques based on Image Classification and Object Detection to Count Cars and Non-empty Stalls in Parking Spaces

Proceedings of the 15th International Joint Conference on e-Business and Telecommunications ◽

10.5220/0006851404940502 ◽

2018 ◽

Author(s):

D. Di Mauro ◽

A. Furnari ◽

G. Patanè ◽

S. Battiato ◽

G. M. Farinella

Keyword(s):

Object Detection ◽

Image Classification ◽

Comparison Of Techniques

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WORKFLOWFOR TRAINING AND SERVING DEEP LEARNING MODELS FOR IMAGE CLASSIFICATION AND OBJECT DETECTION - APPLICATION TO FAULT DETECTION ON ELECTRIC POLES

10.1049/icp.2021.1557 ◽

2021 ◽

Author(s):

C. Coello ◽

R. Sanchez ◽

S. de Lange ◽

J. Halvorsen ◽

M. Bertani-Økland ◽

...

Keyword(s):

Deep Learning ◽

Fault Detection ◽

Object Detection ◽

Image Classification ◽

Learning Models

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Object Detection Based on Global-Local Saliency Constraint in Aerial Images

Remote Sensing ◽

10.3390/rs12091435 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1435 ◽

Cited By ~ 1

Author(s):

Chengyuan Li ◽

Bin Luo ◽

Hailong Hong ◽

Xin Su ◽

Yajun Wang ◽

...

Keyword(s):

Object Detection ◽

Semantic Information ◽

Feature Fusion ◽

Aerial Images ◽

Natural Image ◽

Optical Remote Sensing ◽

Complex Background ◽

Bounding Boxes ◽

The Impact ◽

Oriented Bounding Boxes

Different from object detection in natural image, optical remote sensing object detection is a challenging task, due to the diverse meteorological conditions, complex background, varied orientations, scale variations, etc. In this paper, to address this issue, we propose a novel object detection network (the global-local saliency constraint network, GLS-Net) that can make full use of the global semantic information and achieve more accurate oriented bounding boxes. More precisely, to improve the quality of the region proposals and bounding boxes, we first propose a saliency pyramid which combines a saliency algorithm with a feature pyramid network, to reduce the impact of complex background. Based on the saliency pyramid, we then propose a global attention module branch to enhance the semantic connection between the target and the global scenario. A fast feature fusion strategy is also used to combine the local object information based on the saliency pyramid with the global semantic information optimized by the attention mechanism. Finally, we use an angle-sensitive intersection over union (IoU) method to obtain a more accurate five-parameter representation of the oriented bounding boxes. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed GLS-Net achieves a state-of-the-art detection performance.

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RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data

Sensors ◽

10.3390/s20061594 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1594

Author(s):

Haifeng Li ◽

Xin Dou ◽

Chao Tao ◽

Zhixiang Wu ◽

Jie Chen ◽

...

Keyword(s):

Remote Sensing ◽

Image Classification ◽

Large Scale ◽

Remote Sensing Image ◽

National Standard ◽

Natural Image ◽

Deep Convolutional Neural Networks ◽

Remote Sensing Image Classification ◽

Crowdsourced Data ◽

Classification Tasks

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.

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