Object detection in optical remote sensing images: A survey and a new benchmark

Li, Ké; Wan, Gang; Cheng, Gong; Meng, Liqiu; Han, Junwei

doi:10.1016/j.isprsjprs.2019.11.023

Cited by 966 publications

(334 citation statements)

References 113 publications

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“…The AP was calculated by using the area under the precision-recall curve (p-r curve) [54][55][56]. The precison and recall were Considering the fact that the number of the samples of the NWPU VHR-10 dataset is limited, the DIOR dataset recently proposed by Li et al [50] was also utilized to verify the effectiveness and generalization of the proposed method in this paper. The DIOR dataset is a large scale benchmark, the size of which is comparable to another well-known large-scale DOTA dataset [51,52].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion

et al. 2020

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The objective of detection in remote sensing images is to determine the location and category of all targets in these images. The anchor based methods are the most prevalent deep learning based methods, and still have some problems that need to be addressed. First, the existing metric (i.e., intersection over union (IoU)) could not measure the distance between two bounding boxes when they are nonoverlapping. Second, the exsiting bounding box regression loss could not directly optimize the metric in the training process. Third, the existing methods which adopt a hierarchical deep network only choose a single level feature layer for the feature extraction of region proposals, meaning they do not take full use of the advantage of multi-level features. To resolve the above problems, a novel object detection method for remote sensing images based on improved bounding box regression and multi-level features fusion is proposed in this paper. First, a new metric named generalized IoU is applied, which can quantify the distance between two bounding boxes, regardless of whether they are overlapping or not. Second, a novel bounding box regression loss is proposed, which can not only optimize the new metric (i.e., generalized IoU) directly but also overcome the problem that existing bounding box regression loss based on the new metric cannot adaptively change the gradient based on the metric value. Finally, a multi-level features fusion module is proposed and incorporated into the existing hierarchical deep network, which can make full use of the multi-level features for each region proposal. The quantitative comparisons between the proposed method and baseline method on the large scale dataset DIOR demonstrate that incorporating the proposed bounding box regression loss, multi-level features fusion module, and a combination of both into the baseline method can obtain an absolute gain of 0.7%, 1.4%, and 2.2% or so in terms of mAP, respectively. Comparing this with the state-of-the-art methods demonstrates that the proposed method has achieved a state-of-the-art performance. The curves of average precision with different thresholds show that the advantage of the proposed method is more evident when the threshold of generalized IoU (or IoU) is relatively high, which means that the proposed method can improve the precision of object localization. Similar conclusions can be obtained on a NWPU VHR-10 dataset.

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Section: Evaluation Metricsmentioning

confidence: 99%

Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion

et al. 2020

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“…A good detector should be of high generalization ability and a good dataset should be a benchmark and guidance in testing and training, respectively. When dataset is relatively interior balanced [14], especially for natural images, such as MS-COCO (Microsoft Common Objects in Context) [15] and Pascal-VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) [16], different architectures of detectors published have similar performance. However, for industrial dataset, which is seriously imbalanced in image quality, size, and background, some techniques do not work anymore.…”

Section: Characteristics Of Fbdf Datasetmentioning

confidence: 99%

“…A good detector should be of high generalization benchmark and guidance in testing and training, respe balanced [14], especially for natural images, such as M Context) [15] and Pascal-VOC (Pattern Analysis, Statisti Visual Object Classes) [16], different architectures of det However, for industrial dataset, which is seriously background, some techniques do not work anymore. It i FBDF in order to train the recognition capability of d highlighted.…”

Section: Characteristics Of Fbdf Datasetmentioning

confidence: 99%

Automatic Fabric Defect Detection Using Cascaded Mixed Feature Pyramid with Guided Localization

Zhang

2020

Sensors

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Generic object detection algorithms for natural images have been proven to have excellent performance. In this paper, fabric defect detection on optical image datasets is systematically studied. In contrast to generic datasets, defect images are multi-scale, noise-filled, and blurred. Back-light intensity would also be sensitive for visual perception. Large-scale fabric defect datasets are collected, selected, and employed to fulfill the requirements of detection in industrial practice in order to address these imbalanced issues. An improved two-stage defect detector is constructed for achieving better generalization. Stacked feature pyramid networks are set up to aggregate cross-scale defect patterns on interpolating mixed depth-wise block in stage one. By sharing feature maps, center-ness and shape branches merges cascaded modules with deformable convolution to filter and refine the proposed guided anchors. After balanced sampling, the proposals are down-sampled by position-sensitive pooling for region of interest, in order to characterize interactions among fabric defect images in stage two. The experiments show that the end-to-end architecture improves the occluded defect performance of region-based object detectors as compared with the current detectors.

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“…The effect of object size is also discussed in the paper. Another survey paper about object detection in remote sensing images by Li et al [58] shows review and comparison among different methods.…”

Section: Object Detectionmentioning

confidence: 99%

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Rabbi

Ray

Schubert³

et al. 2020

Preprint

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The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.

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Object detection in optical remote sensing images: A survey and a new benchmark

Cited by 966 publications

References 113 publications

Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion

Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion

Automatic Fabric Defect Detection Using Cascaded Mixed Feature Pyramid with Guided Localization

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

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