Arbitrary-oriented target detection in large scene sar images

Han, Zishuo; Wang, Chunping; Fu, Qiang

doi:10.1016/j.dt.2019.11.014

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Darknet53 integrates RESNET and contains five residuals in total. Each residuals block is composed of a different number of residuals units, and each residuals unit is composed of two DBL units and residuals operations [27], as shown in Figure 2a. Each DBL cell, in turn, consists of a convolutional layer, a normalized [28], and a Leaky ReLU activation function, as shown in Figure 2b.…”

Section: Yolov3 Network Structurementioning

confidence: 99%

Fast recognition method for citrus under complex environments based on improved YOLOv3

Xiao

Huang

et al. 2021

The Journal of Engineering

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Here, the improved multi-scale YOLO algorithm (Improved-YOLOv3) is presented, which was proposed to realize fast and accurate recognition of citrus fruit in a field environment. With the modification of the YOLO-styled network model, a darknet-53 backbone network with residual modules was designed. A multi-scale detection module was to construct a network model for rapid recognition of citrus fruit in complex environments. Using the improved model to detect and identify citrus fruit targets, the network model can extract more feature information. The improved YOLOv3 model was tested with citrus data, and the detection performance of the improved network and the influence of the number of backbone network layers on the feature extraction capability were compared. The results showed a good detection ability (detection rate, accuracy, map, detection speed) for the target fruit, and the improved YOLOv3 network showed higher accuracy. Moreover, the performance of different training models were compared: the Improved-YOLOv3 has stronger robustness, higher detection accuracy, and shorter training time, and can recognize citrus in complex field environments. The experiment showed that the precision of Improved-YOLOv3 was 90.5% and the accuracy reached 94.3%, the recall rate was 90.3%, the detection time was 9.89 ms per frame, which could provide technical support for this visual recognition system of citrus picking robot.

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Section: Yolov3 Network Structurementioning

confidence: 99%

Fast recognition method for citrus under complex environments based on improved YOLOv3

Xiao

Huang

et al. 2021

The Journal of Engineering

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show abstract

“…However, both of these methods have background limitations, and it is currently difficult to simulate vehicles located in complex, large-scale backgrounds. The third method is background transfer [26][27][28]. Chen et al [26] believe that since the acquisition conditions of the chip image (Chip for short) and the clutter image (Clutter for short) in MSTAR are similar, Chips can be embedded in Clutters to generate vehicle images with large scenes, as shown in Figure 1c.…”

Section: Introductionmentioning

confidence: 99%

Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images

Liu,

Luo,

Wang

2023

Remote Sensing

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Because of the counterintuitive imaging and confusing interpretation dilemma in Synthetic Aperture Radar (SAR) images, the application of deep learning in the detection of SAR targets has been primarily limited to large objects in simple backgrounds, such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478 × 1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with an Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate nine benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex backgrounds.

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“…As road segmentation in SAR images is very important for national economy and people's livelihood, such as transport system, urban development, residential life, and industrial distribution [19], it has been a research hotspot in the field of SAR image interpretation. Yang et al addressed the fusion of image and point cloud data for road detection [45], An proposed a method for extracting roads in complex scenes using the optimized Hough algorithm [1], Fu et al proposed high-resolution remote sensing images road extraction based on wavelet transform and Hough transform [15], Geman et al presented a new approach to tracking roads from satellite images [17], Tsutsui et al presented an approach to road segmentation that only requires image-level annotations at training time [39], and Cheng et al proposed a novel method of fusing geometric and appearance cues for road surface segmentation [5].…”

Section: Introductionmentioning

confidence: 99%

A GF-3 SAR Image Dataset of Road Segmentation

Sun

Zhao²,

Bai³

2021

ITC

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We constructed a GF-3 SAR image dataset based on road segmentation to boost the development of GF-3 synthetic aperture radar (SAR) image road segmentation technology and make GF-3 SAR images be applied to practice better. We selected 23 scenes of GF-3 SAR images in Shaanxi, China, cut them into road chips with 512 × 512 pixels, and then labeled the dataset using LabelMe labeling tool. The dataset consists of 10026 road chips, and these road images are from different GF-3 imaging modes, so there is diversity in resolution and polarization. Three segmentation algorithms such as Multi-task Network Cascades (MNC), Fully Convolutional Instance-aware Semantic Segmentation (FCIS), and Mask Region Convolutional Neural Networks (Mask R-CNN) are trained by using the dataset. The experimental result measures including Average Precision (AP) and Intersection over Union (IoU) show that segmentation algorithms work well with this dataset, and the segmentation accuracy of Mask R-CNN is the best, which demonstrates the validity of the dataset we constructed.

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Arbitrary-oriented target detection in large scene sar images

Cited by 12 publications

References 11 publications

Fast recognition method for citrus under complex environments based on improved YOLOv3

Fast recognition method for citrus under complex environments based on improved YOLOv3

Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images

A GF-3 SAR Image Dataset of Road Segmentation

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