Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Zhou, Liming; Yan, Huijie; Shan, Yingzi; Zheng, Chen; Liu, Yang; Zuo, Xianyu; Qiao, Baojun

doi:10.1155/2021/4685644

Cited by 17 publications

(8 citation statements)

References 34 publications

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“…Experiments are carried out on the remote sensing object detection (RSOD) dataset, and the results show that this method can detect aircraft objects quickly and accurately and obtain good detection performance. Liming Zhou et al 12 proposed a multi-scale detection network (MSDN), which introduced a multi-scale detection system to detect smallsize aircraft. Dawei Li et al 13 developed an efficient multi-frame infrared moving aircraft detection method.…”

Section: Object Detection Methods Based On Deep Learningmentioning

confidence: 99%

Multi-attitude small aircraft object detection based on Gm-APD LiDAR

Ding,

Qu,

Liu

et al. 2024

Advanced Fiber Laser Conference (AFL2023)

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In the military and civil fields, detecting small aircraft is of great significance. In recent years, the rapid development of LiDAR technology has made it possible to detect small aircraft at long distances. However, the scale change and attitude change make the detection difficult. Therefore, a detection network of multi-attitude small aircraft based on LiDAR anchorfree is proposed in this paper. The network structure is improved on the basis of the CenterNet network; using the encoderdecoder network structure, the extended convolutional module is designed to improve the receptive field and obtain the multi-scale information of the object. The IOU sensing branch is added to the detection header of the network to improve the localization accuracy of the object. The experimental results show that the detection accuracy of the improved network on the self-built simulation data set is 2.12% higher than that before the improvement and finally reaches 92.45%. Therefore, using this method can effectively improve the detection accuracy of the object.

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Section: Object Detection Methods Based On Deep Learningmentioning

confidence: 99%

Multi-attitude small aircraft object detection based on Gm-APD LiDAR

Ding,

Qu,

Liu

et al. 2024

Advanced Fiber Laser Conference (AFL2023)

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“…Liming Zhou et al [14] developed a model on Multi-scale Detection Network (MSDN). It can detect small-scale aircrafts even during background noise.…”

Section: Methodsmentioning

confidence: 99%

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Gadamsetty

Rupa

et al. 2022

Water

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Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.

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“…Xu Danqing et al [30] proposed a detection algorithm based on YOLOv3 for the detection of remote sensing targets at different scales, so that the detection targets dominated by small targets can maintain the detection speed and improve the average accuracy. Zhou Liming et al [31] proposed the Multiscale Detection Network (MSDN) to solve the problem that aircraft size is small, and proposed the Deeper and Wider Module (DAWM) to resist the background noise. Finally, the DAWM is introduced into the MSDN and the novel network structure is named the Multiscale Re ned Detection Network (MSRDN).…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Network Method for Remote Sensing Image Based Ground Objects Recognition

Xue

Technology

Zheng

et al. 2022

Preprint

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High resolution remote-sensing images have the characteristics of complex background environment, clustering of objects, etc., which lead to the problem of low accuracy in recognition of large ground objects such as airports, dams, golf field, etc. Based on this problem, this paper proposes a remote sensing image object detection method based on YOLOv5 network. By improving the backbone extraction network, the network structure can be deepened to get more information about large objects, the detection effect can be improved by adding attention mechanism and adding output layer to enhance feature extraction and feature fusion. The pre-training weight is obtained by transfer learning and used as the training weight of the improved YOLOv5 to speed up the network convergence. The experiment is carried out on DIOR dataset, the results show that the improved YOLOv5 network can significantly improve the accuracy of large object recognition compared with the YOLO series network and the EfficientDet model on DIOR datadet, and the mAP of the improved YOLOv5 network is 80.5%, which is 2% higher than the original YOLOv5 network.

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Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Cited by 17 publications

References 34 publications

Multi-attitude small aircraft object detection based on Gm-APD LiDAR

Multi-attitude small aircraft object detection based on Gm-APD LiDAR

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Improved YOLOv5 Network Method for Remote Sensing Image Based Ground Objects Recognition

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