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Automated High-Resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 8922-8940页

作者： Sun, Xian Wang, Peijin Yan, Zhiyuan Diao, Wenhui Lu, Xiaonan Yang, Zhujun Zhang, Yidan Xiang, Deliang Yan, Chen Guo, Jie Dang, Bo Wei, Wei Xu, Feng Wang, Cheng Hansch, Ronny Weinmann, Martin Yokoya, Naoto Fu, Kun Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol Beijing 100190 Peoples R China Beijing Univ Chem Technol Interdisciplinary Res Ctr Artificial Intelligence Beijing 100029 Peoples R China Wuhan Univ Comp Sch Wuhan 430079 Peoples R China Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710060 Peoples R China Fudan Univ Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Xiamen Univ Sch Informat Sci & Engn Fujian Key Lab Sensing & Comp Smart Cities Xiamen 361005 Peoples R China German Aerosp Ctr D-82234 Wessling Germany Karlsruhe Inst Technol Inst Photogrammetry & Remote Sensing D-76131 Karlsruhe Germany Univ Tokyo Grad Sch Frontier Sci Chiba 2778561 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan

In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge.

关键词： Satellites Image segmentation object detection Semantics Optical imaging Marine vehicles Earth Convolutional neural networks Gaofen Challenge object detection and recognition optical images SAR images semantic segmentation

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An ALPR System-based Deep Networks for the detection and recognition 10th

An ALPR System-based Deep Networks for the Detection and Rec...

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10th International Conference on Pattern recognition Applications and Methods (ICPRAM)

作者： Bensouilah, Mouad Zennir, Mohamed Nadjib Taffar, Mokhtar Jijel Univ Dept Comp Sci BP 98 Ouled Aissa 18000 Jijel Algeria

ISBN: (纸本)9789897584862

Automatic license plates reading (ALPR), from images or videos, is a research topic that is still relevant in the field of computer vision. In this article, we propose a new dataset and a robust ALPR system based on the YOLO object detector of literature. The trained Convolutional Neural Networks (CNN) allow us to extract features from license plates and label them through Recurrent Neural Networks (RNN) specialized character recognition. RNN are supported by GRU units instead of LSTM units that are generally used in the literature. The experiments results were conclusive reaching a recognition rate of 92%.

关键词： Computer Vision Convolutional Neural Networks Recurrent Neural Networks GRU LSTM object detection and recognition OCR License Plates recognition

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Simultaneous mass estimation and class classification of scrap metals using deep learning

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RESOURCES CONSERVATION AND RECYCLING 2022年 181卷

作者： Diaz-Romero, Dillam Jossue Van den Eynde, Simon Sterkens, Wouter Engelen, Bart Zaplana, Isiah Dewulf, Wim Goedeme, Toon Peeters, Jef Katholieke Univ Leuven Dept Mech Engn Celestijnenlaan 300 B-3000 Louvain Belgium Katholieke Univ Leuven PSI EAVISE Jan Pieter Nayerlaan 5 B-2860 St Katelijne Waver Belgium Katholieke Univ Leuven Dept Mech Engn Wetenschapspk 27 B-3590 Diepenbeek Belgium

While deep learning has helped improve the performance of classification, object detection, and segmentation in recycling, its potential for mass prediction has not yet been explored. Therefore, this study proposes a system for mass prediction with and without feature extraction and selection, including principal component analysis (PCA). These feature extraction methods are evaluated on a combined Cast (C), Wrought (W) and Stainless Steel (SS) image dataset using state-of-the-art machine learning and deep learning algorithms for mass prediction. After that, the best mass prediction framework is combined with a DenseNet classifier, resulting in multiple outputs that perform both object classification and object mass prediction. The proposed architecture consists of a DenseNet neural network for classification and a backpropagation neural network (BPNN) for mass prediction, which uses up to 24 features extracted from depth images. The proposed method obtained 0.82 R2, 0.2 RMSE, and 0.28 MAE for the regression for mass prediction with a classification performance of 95% for the C & W test dataset using the DenseNet+BPNN+PCA model. The DenseNet+BPNN+None model without the selected feature (None) used for the CW & SS test data had a lower performance for both classification of 80% and the regression (0.71 R2, 0.31 RMSE, and 0.32 MAE). The presented method has the potential to improve the monitoring of the mass composition of waste streams and to optimize robotic and pneumatic sorting systems by providing a better understanding of the physical properties of the objects being sorted.

关键词： Artificial Intelligence Automatic Sorting Metal Recycling Stainless Steel Cast and Wrought Aluminium scrap Deep Learning Computer Vision Backpropagation Neural Network Mass/weight Prediction object detection and recognition

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FisheyeDet: A Self-Study and Contour-Based object Detector in Fisheye Images

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IEEE ACCESS 2020年 8卷 71739-71751页

作者： Li, Tangwei Tong, Guanjun Tang, Hongying Li, Baoqing Chen, Bo Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Sci & Technol Microsyst Lab Shanghai 201800 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Fisheye Images have attracted increasing attention from the research community due to their large field of view (LFOV). However, the geometric transformations inherent in fisheye cameras result in unknown spatial distortion and large variations in the appearance of objects. And this fact leads to poor performance of the state-of-the-art methods in conventional two-dimensional (2D) images. To address this problem, we propose a self-study and contour-based object detector in fisheye images, named FisheyeDet. The No-prior Fisheye Representation Method is proposed to guarantee that the network adaptively extracts distortion features without prior information such as prespecified lens parameters, special calibration patterns, etc. Furthermore, in order to tightly and robustly localize objects in fisheye images, the Distortion Shape Matching strategy is proposed, which invokes the irregular quadrilateral bounding boxes based on the contour of distorted objects as the core. By combining with the "No-prior Fisheye Representation Method" and "Distortion Shape Matching", our proposed detector builds an end-to-end network. Finally, due to the lack of public fisheye datasets, we are on the first attempt to create a multi-class fisheye dataset VOC-Fisheye for object detection. Our proposed detector shows favorable generalization ability and achieves 74.87% mAP (mean average precision) on the VOC-Fisheye, outperforming the existing state-of-the-art methods.

关键词： Distortion Feature extraction Detectors object detection Shape Cameras Kernel Fisheye object detection and recognition large field of view (LFOV) deep learning

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object detection on Spatially Inhomogeneous Backgrounds Using Neural Networks

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OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING 2019年第6期55卷 587-591页

作者： Shakenov, A. K. Russian Acad Sci Inst Automat & Electmetry Siberian Branch Pr Akad Koptyuga 1 Novosibirsk 630090 Russia

Several approaches to the use of neural networks for object detection on spatially inhomogeneous backgrounds are considered. A method for constructing a classifier for object detection directly from observed fragments has been developed. An approach consisting of a combination of matched linear filtering and convolutional neural networks is proposed. It is shown that this approach reduces the false alarm probability while maintaining the object detection probability.

关键词： object detection and recognition convolutional neural networks machine learning small objects

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Smartphone-based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

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JOURNAL OF FIELD ROBOTICS 2020年第3期37卷 404-420页

作者： Martinez-Alpiste, Ignacio Casaseca-de-la-Higuera, Pablo Alcaraz-Calero, Jose M. Grecos, Christos Wang, Qi Univ West Scotland Fac Sci & Technol Sch Engn & Comp Paisley PA1 2BE Renfrew Scotland Univ Valladolid Lab Procesado Imagen ETSI Telecomunicac Valladolid Spain Natl Coll Ireland Sch Comp Dublin Ireland

Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innovative UAS that explores machine learning (ML) capabilities in a smartphone-based mobile platform for object detection and recognition applications. A new system framework tailored to this challenging use case is designed with a customized workflow specified. Furthermore, the design of the embedded ML leverages TensorFlow, a cutting-edge open-source ML framework. The prototype of the system integrates all the architectural components in a fully functional system, and it is suitable for real-world operational environments such as seek and rescue use cases. Experimental results validate the design and prototyping of the system and demonstrate an overall improved performance compared with the state of the art in terms of a wide range of metrics.

关键词： image processing machine learning object detection and recognition smartphone unmanned aerial vehicle image processing smartphone

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Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic object detection: A Comprehensive Review

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IEEE ACCESS 2020年 8卷 170461-170495页

作者： Aziz, Lubna Haji Salam, Md. Sah Bin Sheikh, Usman Ullah Ayub, Sara BUITMES Dept Comp Engn Quetta 87100 Pakistan UTM Dept Comp Engn Johor Baharu 81310 Malaysia UTM Sch Elect Engn Fac Comp Johor Baharu 81310 Malaysia UTM Sch Comp Fac Engn Johor Baharu 81310 Malaysia UTM Fac Elect Engn Johor Baharu 81310 Malaysia

object detection is a fundamental but challenging issue in the field of generic image analysis;it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R-CNN, SPPnet, Fast R-CNN, Faster R-CNN, Mask RCN, RFCN, FPN, 2) classification/regression base object detection methods such as YOLO(v2 to v5), SSD, DSSD, RetinaNet, RefineDet, CornerNet, EfficientDet, M2Det 3) Some latest detectors such as, relation network for object detection, DCN v2, NAS FPN. Moreover, five publicly available benchmark datasets and their standard evaluation metrics are also discussed. We mainly focus on the application of deep learning architectures to five major applications, namely object detection in Surveillance, Military, Transportation, Medical, and Daily Life. In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational power. Finally, we finish the survey by identifying fifteen current trends and promising direction for future research.

关键词： object detection Machine learning Face detection Computer architecture Feature extraction Market research Face object detection and recognition deep learning convolutional neural networks (CNN) neural network

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object detection based on SSD-ResNet 6

Object detection based on SSD-ResNet

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6th IEEE International Conference on Cloud Computing and Intelligence Systems (IEEE CCIS)

作者： Lu, Xin Kang, Xin Nishide, Shun Ren, Fuji Tokushima Univ Tokushima Japan Tokushima Univ Fac Engn Tokushima Japan

ISBN: (纸本)9781728138633

Nowadays, with the abundance and diversity of the data sets of the detected objects, detection and recognition technology has achieved excellent performance in learning effect. However, because the target objects are usually very small in many real-world applications while the background environment seldom varies, manually annotating these objects is extremely costly in time and manpower. These problems have challenged the learning effect of standard neural networks. In this paper, we propose a novel method to replace the original network structure and to extend the number of layers for detecting many kinds of dangerous goods among different background environments. Specifically, we employ SSD as the basic network structure and replace the inside VGG 16 with a ResNet101 network. The experimental results show that the ResNet network is effective in detecting many kinds of dangerous objects in small data sets. The proposed model outperforms other neural networks in learning efficiency and accuracy.

关键词： SSD ResNet CNN object detection and recognition

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detection and recognition of batteries on X-Ray images of waste electrical and electronic equipment using deep learning

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RESOURCES CONSERVATION AND RECYCLING 2021年 168卷 105246-105246页

作者： Sterkens, Wouter Diaz-Romero, Dillam Goedeme, Toon Dewulf, Wim Peeters, Jef R. Katholieke Univ Leuven Dept Mech Engn B-3001 Leuven Belgium Katholieke Univ Leuven PSI EAVISE B-2860 St Katelijne Waver Belgium

The trend of increased use of lithium-ion batteries, challenges the cost-effectiveness and safety of manual battery separation during the end-of-life treatment of Waste Electric and Electronic Equipment (WEEE). Therefore, the need for novel techniques to separate and sort batteries from WEEE is increasingly important. For this reason, the presented research investigates the potential to facilitate the development of novel techniques for battery extraction and sorting by examining the technical feasibility of predicting the presence, location, and type of batteries inside electronic devices with a deep learning object detection network using X-Ray images of the internal structure of WEEE. To determine the required X-ray imaging parameters, 532 electronic devices were arbitrarily collected from a recycling facility. From each product, two X-Ray Transmission (XRT) images were captured at two different X-Ray source configurations. Results obtained with the limited dataset are promising, demonstrating a 91% true positive rate and only a 6% false positive rate for classifying battery-containing devices. Moreover, a precision of 89% and a recall of 81% are demonstrated for battery detection, and an average precision of 85% and an average recall of 76% are demonstrated to distinguish amongst the following six battery technologies: cylindrical nickel-metal hydride or nickel-cadmium, cylindrical alkaline, cylindrical zinc-carbon, cylindrical lithium-ion, pouch lithium-ion, and button cell batteries. These results demonstrate the potential of using deep learning object detection on XRT-generated images for both automated battery extraction and sorting, regardless of the condition or shape of the products.

关键词： Waste electrical and electronic equipment Battery sorting and recycling X-Ray Transmission imaging Deep learning computer vision object detection and recognition

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Deep learning computer vision for the separation of Cast- and Wrought-Aluminum scrap

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RESOURCES CONSERVATION AND RECYCLING 2021年 172卷 105685-105685页

作者： Diaz-Romero, Dillam Sterkens, Wouter Van den Eynde, Simon Goedeme, Toon Dewulf, Wim Peeters, Jef Katholieke Univ Leuven Dept Mech Engn B-3001 Leuven Belgium PSI EAVISE KU Leuven B-2860 St Katelijne Waver Belgium

In consequence of the electrification and the increased adoption of lightweight structures in the automotive industry, global demand for wrought Aluminum (Al) is expected to rise while demand for cast Al will stagnate. Since cast alloys can only be converted to wrought alloys by energy-intensive processes, the most promising strategy to avoid the emergence of excess Al cast alloys scrap is to sort cast from wrought alloys. To date, the separation of complex mixes of non-ferrous metals often implies the use of either or both sink-float techniques and/or X-ray fluorescence (XRF) based sorting. Therefore, the presented research develops an efficient method to classify cast and wrought (C&W) alloys in a real-time system with a conveyor belt using transfer learning methods, such as fine-tuning and feature extraction. Five CNNs are evaluated to classify C&W alloys using colour and depth images and transfer learning methods. In addition, the early fusion and late fusion of colour and depth images of C&W Al are investigated. For early fusion, data is added as an extra input channel to the first convolution layer of the CNN, and for later fusion, the images are fed in two separate subnetworks with the same architecture, where the parameters of the fully-connected layers are concatenated in both subnetworks. Our approach shows that late fusion CNN DenseNet allows obtaining the best performances and can achieve up to 98% accuracy.

关键词： Artificial intelligence Automatic sorting Scrap recycling Cast and wrought Aluminum Deep learning computer vision object detection and recognition

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