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Convolutional Neural Networks for Object Detection

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 56648

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Dr. Mercedes E. Paoletti

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Department of Computer Architecture and Automation, Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
Interests: hyperspectral image fusion; hyperspectral classification; high-order tensor analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Juan M. Haut

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Department of Computer Technology and Communications, Polytechnic School of Cáceres, University of Extremadura, 10003 Cáceres, Spain
Interests: hyperspectral image analysis; machine (deep) learning; neural networks; multisensor data fusion; high performance computing; cloud computing
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Special Issue Information

Dear Colleagues,

Object detection is a fundamental problem within remote sensing imaging analysis. Recent advances in hardware and software capabilities have allowed for the development of powerful machine-learning-based object detection techniques. In particular, deep learning models have received increased interest due to their great potential for extracting very abstract and descriptive feature data representations from original inputs. Instead of widely used shallow architectures and traditional handcrafted feature processing, deep learning methods offer a great variety of very deep architectures based on stacking layers, which extract increasingly complex and abstract features from the input data in a successive and hierarchical way. In this context, convolutional-based neural models have demonstrated a great generalization power coupled with a strong and automatic feature extraction capability, allowing them to reach an outstanding performance and positioning themselves as the current state of the art in many tasks related to computer vision, in particular in image classification tasks.

In this sense, object detection requires going a step further, as it involves not only classifying images but also locating objects of different classes in different positions/orientations and with different sizes within the images, with the aim of providing a more complete image understanding. This imposes the detailed processing of a huge amount of geometric and spatial information contained in the remote sensed image. Even with so much literature devoted to this topic, the development of powerful and efficient deep learning models remains a challenging task, due to the limitations of convolutional architectures (rotations, spatial relations, black box nature, etc.) and the characteristics of remotely sensed images (different spatial/spectral resolutions, atmospheric noises, sensor limitations, etc.). In this sense, there is still so much we do not know about deep learning models related to object detection in the remote sensing field.

This Special Issue aims to foster the application of advanced deep learning algorithms to perform accurate object detection applied within the remote sensing field, and it is an excellent opportunity for the dissemination of recent results and cooperation for further innovations.

For this Special Issue, we welcome contributions related, but not limited to, the following:

Deep learning, convolutional neural networks, hybrid architectures, etc. for object detection;
Improvements in deep learning model capabilities for extracting and learning features of interest within object detection tasks, such as context- and attention-based mechanisms, among others;
Detection of small or occluded objects and/or in challenging conditions;
Real-time or fast models for object detection;
Improvements in localization accuracy.

Dr. Mercedes E. Paoletti
Dr. Juan M. Haut
Guest Editors

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Keywords

Remote sensing
Machine learning
Deep learning
Transfer learning
Convolutional neural network
Recurrent neural network
Object detection

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Published Papers (8 papers)

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19 pages, 2494 KiB

Open AccessArticle

Water Body Extraction in Remote Sensing Imagery Using Domain Adaptation-Based Network Embedding Selective Self-Attention and Multi-Scale Feature Fusion

by Jiahang Liu and Yue Wang

Remote Sens. 2022, 14(15), 3538; https://doi.org/10.3390/rs14153538 - 23 Jul 2022

Cited by 9 | Viewed by 3055

Abstract

A water body is a common object in remote sensing images and high-quality water body extraction is important for some further applications. With the development of deep learning (DL) in recent years, semantic segmentation technology based on deep convolution neural network (DCNN) brings a new way for automatic and high-quality body extraction from remote sensing images. Although several methods have been proposed, there exist two major problems in water body extraction, especially for high resolution remote sensing images. One is that it is difficult to effectively detect both large and small water bodies simultaneously and accurately predict the edge position of water bodies with DCNN-based methods, and the other is that DL methods need a large number of labeled samples which are often insufficient in practical application. In this paper, a novel SFnet-DA network based on the domain adaptation (DA) embedding selective self-attention (SSA) mechanism and multi-scale feature fusion (MFF) module is proposed to deal with these problems. Specially, the SSA mechanism is used to increase or decrease the space detail and semantic information, respectively, in the bottom-up branches of the network by selective feature enhancement, thus it can improve the detection capability of water bodies with drastic scale change and can prevent the prediction from being affected by other factors, such as roads and green algae. Furthermore, the MFF module is used to accurately acquire edge information by changing the number of the channel of advanced feature branches with a unique fusion method. To skip the labeling work, SFnet-DA reduces the difference in feature distribution between labeled and unlabeled datasets by building an adversarial relationship between the feature extractor and the domain classifier, so that the trained parameters of the labeled datasets can be directly used to predict the unlabeled images. Experimental results demonstrate that the proposed SFnet-DA has better performance on water body segmentation than state-of-the-art methods. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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18 pages, 3725 KiB

Open AccessArticle

High Quality Object Detection for Multiresolution Remote Sensing Imagery Using Cascaded Multi-Stage Detectors

by Binglong Wu, Yuan Shen, Shanxin Guo, Jinsong Chen, Luyi Sun, Hongzhong Li and Yong Ao

Remote Sens. 2022, 14(9), 2091; https://doi.org/10.3390/rs14092091 - 27 Apr 2022

Cited by 5 | Viewed by 2447

Abstract

Deep-learning-based object detectors have substantially improved state-of-the-art object detection in remote sensing images in terms of precision and degree of automation. Nevertheless, the large variation of the object scales makes it difficult to achieve high-quality detection across multiresolution remote sensing images, where the quality is defined by the Intersection over Union (IoU) threshold used in training. In addition, the imbalance between the positive and negative samples across multiresolution images worsens the detection precision. Recently, it was found that a Cascade region-based convolutional neural network (R-CNN) can potentially achieve a higher quality of detection by introducing a cascaded three-stage structure using progressively improved IoU thresholds. However, the performance of Cascade R-CNN degraded when the fourth stage was added. We investigated the cause and found that the mismatch between the ROI features and the classifier could be responsible for the degradation of performance. Herein, we propose a Cascade R-CNN++ structure to address this issue and extend the three-stage architecture to multiple stages for general use. Specifically, for cascaded classification, we propose a new ensemble strategy for the classifier and region of interest (RoI) features to improve classification accuracy at inference. In localization, we modified the loss function of the bounding box regressor to obtain higher sensitivity around zero. Experiments on the DOTA dataset demonstrated that Cascade R-CNN++ outperforms Cascade R-CNN in terms of precision and detection quality. We conducted further analysis on multiresolution remote sensing images to verify model transferability across different object scales. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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18 pages, 1717 KiB

Open AccessArticle

Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection

by Rukhshanda Hussain, Yash Karbhari, Muhammad Fazal Ijaz, Marcin Woźniak, Pawan Kumar Singh and Ram Sarkar

Remote Sens. 2021, 13(23), 4941; https://doi.org/10.3390/rs13234941 - 5 Dec 2021

Cited by 36 | Viewed by 3764

Abstract

Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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22 pages, 6871 KiB

Open AccessArticle

Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion

by Minghua Zhang, Shubo Xu, Wei Song, Qi He and Quanmiao Wei

Remote Sens. 2021, 13(22), 4706; https://doi.org/10.3390/rs13224706 - 21 Nov 2021

Cited by 114 | Viewed by 9647

Abstract

A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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20 pages, 10445 KiB

Open AccessArticle

Advancing Tassel Detection and Counting: Annotation and Algorithms

by Azam Karami, Karoll Quijano and Melba Crawford

Remote Sens. 2021, 13(15), 2881; https://doi.org/10.3390/rs13152881 - 23 Jul 2021

Cited by 17 | Viewed by 3662

Abstract

Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering. In this article, three state-of-the-art DL techniques, CenterNet based on point annotation, task-aware spatial disentanglement (TSD), and detecting objects with recursive feature pyramids and switchable atrous convolution (DetectoRS) based on bounding box annotation, are modified to improve their performance for this application and evaluated for tassel detection relative to Tasselnetv2+. The dataset for the experiments is comprised of RGB images of maize tassels from plant breeding experiments, which vary in size, complexity, and overlap. Results show that the point annotations are more accurate and simpler to acquire than the bounding boxes, and bounding box-based approaches are more sensitive to the size of the bounding boxes and background than point-based approaches. Overall, CenterNet has high accuracy in comparison to the other techniques, but DetectoRS can better detect early-stage tassels. The results for these experiments were more robust than Tasselnetv2+, which is sensitive to the number of tassels in the image. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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21 pages, 60660 KiB

Open AccessArticle

Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

by Syed Muhammad Arsalan Bashir and Yi Wang

Remote Sens. 2021, 13(9), 1854; https://doi.org/10.3390/rs13091854 - 10 May 2021

Cited by 48 | Viewed by 7858

Abstract

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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26 pages, 10839 KiB

Open AccessArticle

YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images

by Minh-Tan Pham, Luc Courtrai, Chloé Friguet, Sébastien Lefèvre and Alexandre Baussard

Remote Sens. 2020, 12(15), 2501; https://doi.org/10.3390/rs12152501 - 4 Aug 2020

Cited by 107 | Viewed by 16952

Abstract

Object detection from aerial and satellite remote sensing images has been an active research topic over the past decade. Thanks to the increase in computational resources and data availability, deep learning-based object detection methods have achieved numerous successes in computer vision, and more recently in remote sensing. However, the ability of current detectors to deal with (very) small objects still remains limited. In particular, the fast detection of small objects from a large observed scene is still an open question. In this work, we address this challenge and introduce an enhanced one-stage deep learning-based detection model, called You Only Look Once (YOLO)-fine, which is based on the structure of YOLOv3. Our detector is designed to be capable of detecting small objects with high accuracy and high speed, allowing further real-time applications within operational contexts. We also investigate its robustness to the appearance of new backgrounds in the validation set, thus tackling the issue of domain adaptation that is critical in remote sensing. Experimental studies that were conducted on both aerial and satellite benchmark datasets show some significant improvement of YOLO-fine as compared to other state-of-the art object detectors. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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17 pages, 4690 KiB

Open AccessTechnical Note

Smart Count System Based on Object Detection Using Deep Learning

by Jiwon Moon, Sangkyu Lim, Hakjun Lee, Seungbum Yu and Ki-Baek Lee

Remote Sens. 2022, 14(15), 3761; https://doi.org/10.3390/rs14153761 - 5 Aug 2022

Cited by 6 | Viewed by 6468

Abstract

Object counting is an indispensable task in manufacturing and management. Recently, the development of image-processing techniques and deep learning object detection has achieved excellent performance in object-counting tasks. Accordingly, we propose a novel small-size smart counting system composed of a low-cost hardware device and a cloud-based object-counting software server to implement an accurate counting function and overcome the trade-off presented by the computing power of local hardware. The cloud-based object-counting software consists of a model adapted to the object-counting task through a novel DBC-NMS (our own technique) and hyperparameter tuning of deep-learning-based object-detection methods. With the power of DBC-NMS and hyperparameter tuning, the performance of the cloud-based object-counting software is competitive over commonly used public datasets (CARPK and SKU110K) and our custom dataset of small pills. Our cloud-based object-counting software achieves an mean absolute error (MAE) of 1.03 and a root mean squared error (RMSE) of 1.20 on the Pill dataset. These results demonstrate that the proposed smart counting system accurately detects and counts densely distributed object scenes. In addition, the proposed system shows a reasonable and efficient cost–performance ratio by converging low-cost hardware and cloud-based software. Full article

(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)

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