Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images

Qu, Junsuo; Su, Chang; Zhang, Zhiwei; Razi, Abolfazl

doi:10.1109/access.2020.2991439

Cited by 47 publications

(23 citation statements)

References 33 publications

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“…Feature pyramid was used to fuse four feature layers to get four new feature layers, finally six layers were used to detecte objects. JunSuo Qu proposed a dilated convolution and feature fusion single shot multibox detector [20] (DFSSD), which emplyed dilated convolution to operate the Conv3_3 layer so that the third layer features directly participate in the prediction. Fusion module was adopted to combines the high-level feature map with the low-level feature map, and seven feature layers were used to detect remote sensing small objects.…”

Section: Related Workmentioning

confidence: 99%

Small Object Recognition Algorithm of Grain Pests Based on SSD Feature Fusion

et al. 2021

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The detection of grain pests is of great significance to grain storage. However, in practice, because the size of grain insects is too small to identify. In this paper, the feature fusion SSD(single shot multi-box detector) algorithm based on the Top-Down strategy was proposed. Firstly, the Top-Down module is used to fuse the output characteristics of conv4 and conv5, and the block 11 which is not conducive to small object detection is deleted. Secondly, K-means clustering algorithm is used to cluster prior bounding boxes and made them more suitable for grain pests, which improves the performance on small object detection of grain pests. Five methods were used to enhance the self-made dataset of grain pests, and the enhanced dataset reached 9990 images. Experiments on the enhanced dataset show that the optimized model achieves a mAP (mean Average Precision) 96.89% with detection speed of 0.040s per image. Compared with 95.45% of the mAP achieved by the original SSD algorithm, the proposed model has a great improvement on detection performance. Compared with two-stages Faster R-CNN (mAP is 90.53% and speed is 0.115s per image), YOLOv3, TDFSSD and EfficentDet (D2, D1), the speed and accuracy of the optimized SSD algorithm have obvious advantages. The experimental results show that the proposed SSD model has a good performance on small object detection of grain pests and has a certain guiding significance for subsequent grain pest image detection.

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Section: Related Workmentioning

confidence: 99%

Small Object Recognition Algorithm of Grain Pests Based on SSD Feature Fusion

et al. 2021

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“…Then, through lightweight processing, the model partly reduces the redundant parameters increased by the augmented dimensions of output feature maps [34]. J. Qu et al proposed DFSSD to increase detection accuracy for small objects in remote sensing images [35]. Different from the original SSD, in this algorithm, the random clipping procedures of data preprocessing layers are discarded, and a feature pyramid network (FPN) is added to enhance information of low-level feature map.…”

Section: Related Workmentioning

confidence: 99%

An Approach on Image Processing of Deep Learning Based on Improved SSD

Jin

Liu

2021

Symmetry

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Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

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“…In [14], an architecture with five dilated branches was designed manually to solve the receptive field issue, providing an input including multi-scale information for subsequent feature fusion. In [15], a detector was employed, which configures the receptive field enhancement block after the multi-scale extraction module, bringing significant improvement to the performance of small target detection. In [16], an object classification backbone architecture was designed to improve the resolution change of the sampling process, thus solving the problem of multi-scale object recognition.…”

Section: Dilated Rate Strategy For Object Detectionmentioning

confidence: 99%

Ship Detection via Dilated Rate Search and Attention-Guided Feature Representation

Zhi

Shi

et al. 2021

Remote Sensing

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Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the target scale based on training data. Moreover, most of them ignore the effective retention of position information in the feature extraction process, which reduces the contribution of features to subsequent classification. To overcome these limitations, we propose a novel ship detection framework combining the dilated rate selection and attention-guided feature representation strategies, which can efficiently detect ships of different scales under the interference of complex environments such as clouds, sea clutter and mist. Specifically, we present a dilated convolution parameter search strategy to adaptively select the dilated rate for the multi-branch extraction architecture, adaptively obtaining context information of different receptive fields without sacrificing the image resolution. Moreover, to enhance the spatial position information of the feature maps, we calculate the correlation of spatial points from the vertical and horizontal directions and embed it into the channel compression coding process, thus generating the multi-dimensional feature descriptors which are sensitive to direction and position characteristics of ships. Experimental results on the Airbus dataset demonstrate that the proposed method achieves state-of-the-art performance compared with other detection models.

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Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images

Cited by 47 publications

References 33 publications

Small Object Recognition Algorithm of Grain Pests Based on SSD Feature Fusion

Small Object Recognition Algorithm of Grain Pests Based on SSD Feature Fusion

An Approach on Image Processing of Deep Learning Based on Improved SSD

Ship Detection via Dilated Rate Search and Attention-Guided Feature Representation

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