Fast Airplane Detection Based on Multi-Layer Feature Fusion of Fully Convolutional Networks

Peng, 辛鹏 Xin; Yuelei, 许悦雷 Xu; Hong, 唐红 Tang; Shiping, 马时平 Ma; Shuai, 李帅 Li; Chao, 吕超 Lü

doi:10.3788/aos201838.0315003

Cited by 4 publications

(2 citation statements)

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“…Huang et al (2017) combined SSD algorithm with Densenet network and FPN to improve SSD algorithm and detect objects. Xin Peng et al (2018) and Chen et al (2018) proposed an aircraft object detection algorithm based on a multi‐scale SSD network, which improved the detection accuracy of multi‐scale aircraft objects in RSIs, but it significantly reduced the detection speed. Meanwhile, the detection accuracy of this method for small objects was poor.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning‐based single shot multibox detector model for object detection in optical remote sensing images

Wang

Yin

Alyami

et al. 2022

Geoscience Data Journal

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Remote sensing image object detection is widely used in civil and military fields. The important task is to detect objects such as ships, planes, airports, harbours and so on, and then it can obtain object category and position information. It is of great significance to use remote sensing images to observe the densely arranged and directional targets such as cars and ships parked in parking lots and harbours. The object detection task mainly includes object localization and classification. Remote sensing images contain large number of small objects and dense scenes due to the long shooting distance and wide coverage. Small objects occupy few pixels in the image, and they are easily miss‐detected. In dense scenes, the overlapping part of each object is large, so it is easy to detect objects repeatedly. The traditional small object detection methods deliver low accuracy and take long time. Therefore, object detection is very challenging. We put forward a novel deep learning‐based single shot multibox detector (SSD) model for object detection. First, we propose an improved inception network to optimize SSD to strengthen the small object feature extraction ability (FEA) in the shallow network. Second, the feature pyramid network is modified to enhance the fusion effect. Third, the deep feature fusion module is designed to improve the FEA of the deep network. Finally, the extracted image features are matched with candidate boxes with different aspect ratios to perform object detection and location with different scales. Experiments on DOTA show that the proposed algorithm can adapt to the remote sensing object detection in different backgrounds, and effectively improve the detection effect of remote sensing objects in complex scenes.

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Section: Introductionmentioning

confidence: 99%

A novel deep learning‐based single shot multibox detector model for object detection in optical remote sensing images

Wang

Yin

Alyami

et al. 2022

Geoscience Data Journal

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“…With the existing target detection methods, it is tricky to detect pin defects in large-scale aerial transmission line images with complex backgrounds. The main technical difficulties are as follows: (1) the images are blurred due to jitter and light intensity during collection by the drone; (2) the image background is complex, the detection target is small and occluded; (3) high-precision convolutional neural networks generally have a profound number of layers [13][14], and the resulting calculation and storage costs are enormous; (4) a deeper network structure requires a lot of labeled data during training to ensure high detection accuracy, and the training is complex and inefficient; and (5) since existing target detection algorithms are designed for conventional images, the image size must be fixed during training and detection [15][16][17][18]. Thus, we cannot achieve versatility and scale adaptability in the target detection of aerial transmission line images.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pin Defects in Aerial Images Based on Cascaded Convolutional Neural Network

Xiao

Zhang

et al. 2021

IEEE Access

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Pins are standard fasteners in power transmission lines, and the hidden dangers of pins falling off dramatically affects their safe operation. If a pin is missed, it is called pin defects in this paper. As the pin is a small target and has a complex background, traditional detection algorithms were used to identify pin defects from aerial images which suffer from poor accuracy and low efficiency. This paper proposed a target detection method based on cascaded convolutional neural networks. First, a small-scale shallow full convolutional neural network was used to obtain the region of interest; then, a deeper convolutional neural network conducted target classification and positioning on the obtained region of interest. Next, a nonlinear multilayer perceptron was introduced, the convolution kernel was decomposed, and the multi-scale feature maps were fused. At this point, an angle variable was added to the classification cross-entropy loss function. Multi-task learning and offline hard sample mining strategies were used in the training phase. The proposed method was tested on a self-built pin dataset and the remote sensing image RSOD dataset, and the experimental results proved its effectiveness. Our method can accurately identify pin defects in aerial images, thereby solving the engineering application problem of pin defect detection in transmission lines.

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Deep and shallow features fusion based on deep convolutional neural network for speech emotion recognition

Sun

Xie

2018

Int J Speech Technol

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Fast Airplane Detection Based on Multi-Layer Feature Fusion of Fully Convolutional Networks

Cited by 4 publications

References 0 publications

A novel deep learning‐based single shot multibox detector model for object detection in optical remote sensing images

A novel deep learning‐based single shot multibox detector model for object detection in optical remote sensing images

Detection of Pin Defects in Aerial Images Based on Cascaded Convolutional Neural Network

Deep and shallow features fusion based on deep convolutional neural network for speech emotion recognition

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