A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

Ma, Wenping; Guo, Qiongqiong; Wu, Yue; Zhao, Wei; Zhang, Xiangrong; Jiao, Licheng

doi:10.3390/rs11070737

Cited by 37 publications

(18 citation statements)

References 53 publications

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“…In general, the training and detection procedure of faster R-CNN are summarized as follows: We evaluated our method based on mean average precision (mAP) and false positive rate which is a metric for the false alarm probability as proposed by [13,25,28,41]. These are vital evaluation parameters as used by [43]. Thus, the algorithm performs best when the mean average precision is high and vice versa.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

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Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Abubakar

Liu

et al. 2019

Journal of Sensors

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Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.

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Section: Journal Of Sensorsmentioning

confidence: 99%

“…The IoU was computed using equation (1). The precision and the recall and false positive rate are given by [38,43]…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Abubakar

Liu

et al. 2019

Journal of Sensors

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“…Recently, remote sensing image has been studied in more and more areas, including image registration [1][2][3], change detection [4,5], object detection [6] and so on. As is known to all, Hyperspectral Imaging (HSI) is a special type of remote sensing image which has abundant spectral and spatial information [7], and has been studied in many fields, including forest vegetation cover monitoring [8], classification of land-use [9,10], change area detection [11], anomaly detection [12] and environmental protection [13].…”

Section: Introductionmentioning

confidence: 99%

Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

et al. 2019

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Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.

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“…On this basis, object detection in remote sensing imagery has been widely studied in recent years [27][28][29][30][31][32]. In the field of remote sensing, many researchers have made great efforts to object detection methods based on CNN [33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the Faster R-CNN [20], a multi-model decision fusion network developed in [34] combines a contextual information fusion sub-network, a part-based multi-region fusion sub-network, and a baseline sub-network to recognize and locate geospatial objects. In addition, the final detection results are obtained by the way of making a decision fusion on the results of the three sub-networks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

Xie

Qin

et al. 2019

Remote Sensing

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With great significance in military and civilian applications, the topic of detecting small and densely arranged objects in wide-scale remote sensing imagery is still challenging nowadays. To solve this problem, we propose a novel effectively optimized one-stage network (NEOON). As a fully convolutional network, NEOON consists of four parts: Feature extraction, feature fusion, feature enhancement, and multi-scale detection. To extract effective features, the first part has implemented bottom-up and top-down coherent processing by taking successive down-sampling and up-sampling operations in conjunction with residual modules. The second part consolidates high-level and low-level features by adopting concatenation operations with subsequent convolutional operations to explicitly yield strong feature representation and semantic information. The third part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the fore part of the network where the information of small objects exists. The final part is achieved by four detectors with different sensitivities accessing the fused features, all four parallel, to enable the network to make full use of information of objects in different scales. Besides, the Focal Loss is set to enable the cross entropy for classification to solve the tough problem of class imbalance in one-stage methods. In addition, we introduce the Soft-NMS to preserve accurate bounding boxes in the post-processing stage especially for densely arranged objects. Note that the split and merge strategy and multi-scale training strategy are employed in training. Thorough experiments are performed on ACS datasets constructed by us and NWPU VHR-10 datasets to evaluate the performance of NEOON. Specifically, 4.77% and 5.50% improvements in mAP and recall, respectively, on the ACS dataset as compared to YOLOv3 powerfully prove that NEOON can effectually improve the detection accuracy of small objects in remote sensing imagery. In addition, extensive experiments and comprehensive evaluations on the NWPU VHR-10 dataset with 10 classes have illustrated the superiority of NEOON in the extraction of spatial information of high-resolution remote sensing images.

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A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

Cited by 37 publications

References 53 publications

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

A Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

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