Aircraft detection in satellite imagery using deep learning-based object detectors

Azam, Basim; Khan, Muhammad Jaleed; Bhatti, Farrukh Aziz; Maud, Abdur Rahman; Hussain, Syed Fawad; Hashmi, Ali; Khurshid, Khurram

doi:10.1016/j.micpro.2022.104630

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…In this section, two main experiments are implemented using Faster RCNN with Resnet-50 as the foundation CNN. Faster RCNN has shown to be better framework for detecting aircraft and is ideal for real-world with little training data situations (Alganci et al, 2020;Azam et al, 2022). In the first experiment, Faster RCNN model is trained by aerial set as shown in…”

Section: Proposed Methodologymentioning

confidence: 99%

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Performance Assessment of Object Detection From Multi Satellites and Aerial Images

Ahmed,

El-Sheimy,

Leung

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Object detection in remote sensing imagery plays an important role in many applications, such as tracking and change detection. With the development of deep learning algorithms and advancement in hardware systems, improved accuracies have been achieved in the detection of various objects from remote sensing images. However, object detection across heterogeneous remote sensing imagery remains an important issue, particularly for satellite and aerial imagery. The colour variation for the same ground objects, variable resolutions, different platform heights, the parallax effect, and image distortion brought on by diverse shooting angles are the biggest hurdles in satellite-aerial detection applications. The research aims to obtain successful model for detecting aircrafts from satellite and aerial images and reduce cost and the gap of revisit time between sensors. The networks were tested using aerial, GF-2, Jilin-1 (JL-1) and Pleiades satellites test sets after being trained individually using the RGB high-resolution aerial set and panchromatic low-resolution GF-2 satellite set to validate the efficiency of the trained models. Also, the aerial-trained model and GF-2 satellite-trained model as dedicated models were compared with each other, and model trained by all dataset for Object Detection in Aerial Images (DOTA). It is observed that the anchor sizes and augmentation methods can enhance the performance of detection models. k-means algorithm and data augmentation were applied to produce better anchor box selection and avoid overfitting, atmospheric conditions problems, respectively. The accuracy assessment results demonstrate that the aerial-trained model outperforms the GF-2 satellite-trained model. In addition, the results of two dedicated detection models show improved accuracy compared to the DOTA-trained model.

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Section: Proposed Methodologymentioning

confidence: 99%

“…Hyperparameters good selection of parameters, such as optimizer function, number of epochs, and learning rate is very important to train architecture. The Stochastic Gradient Descent Method (SGDM) typically delivers good results for transfer learning and Adam performs better when starting from scratch (Azam et al, 2022).…”

Section: 21mentioning

confidence: 99%

Performance Assessment of Object Detection From Multi Satellites and Aerial Images

Ahmed,

El-Sheimy,

Leung

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Excessive depth can lead to feature maps with very low resolution, contributing to a deterioration in object detection performance. Faster RCNN surpassed other detectors, achieving the highest AP of 0.97 when utilizing ResNet-50 as the backbone feature extraction network (Azam et al, 2022).…”

mentioning

confidence: 99%

“…Furthermore, these object detectors outperform region proposalbased object detectors by at least a factor of five. On the other hand, Faster-RCNN with Resnet 50 got 1.7 seconds per frame which is slow for RADAR detection but as RADAR data is very challenging, so we need high performance, which is provided on faster RCNN with Resnet 50, in addition to the comparison done using only single GPU but in the case of RADAR detection it will be available hardware with high specifications which will increase the detection speed (Azam et al, 2022).…”

mentioning

confidence: 99%

Deep Learning for Object Detection Using Radar Data

Reda,

El-Sheimy,

Moussa

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations.

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“…O YOLO é uma ferramenta da visão computacional para detecção e classificação de objetos em tempo real (SALMAN et al, 2022). Propõe o uso de uma rede neural de ponta a ponta que faz todas as suas previsões de bounding boxes e probabilidades de classes, atingindo resultados ótimos, superando outros algoritmos de detecção de objetos em tempo real com uma grande margem (AZAM et al, 2022).…”

Section: You Only Look Once (Yolo)unclassified

Desenvolvimento de um sistema de detecção e rastreamentode veículos para análise de anomalias de tráfego em rodovias utilizando estruturas espaciais e temporais por meio de Visão Computacional

Reyna

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Este trabalho é dedicado aos meus pais ♡, Jonatan e Ana, por me ensinarem a ser uma pessoa de princípios, motivando-me a explorar o mundo e encorajando-me a perseguir meus objetivos. Eles têm sido uma fonte inesgotável de inspiração e apoio em minha jornada, moldando quem sou e me incentivando a sempre buscar o melhor em tudo que faço. Suas lições e amor incondicional são o alicerce sobre o qual construí minha vida, e sou profundamente grata por tudo o que tem feito por mim. RESUMOREYNA, A. H. Desenvolvimento de um sistema de detecção e rastreamento de veículos para análise de anomalias de tráfego em rodovias utilizando estruturas espaciais e temporais por meio de Visão Computacional.. 2024. 96 p. Dissertação (Mestrado em Ciências

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Aircraft detection in satellite imagery using deep learning-based object detectors

Cited by 11 publications

References 34 publications

Performance Assessment of Object Detection From Multi Satellites and Aerial Images

Performance Assessment of Object Detection From Multi Satellites and Aerial Images

Deep Learning for Object Detection Using Radar Data

Desenvolvimento de um sistema de detecção e rastreamentode veículos para análise de anomalias de tráfego em rodovias utilizando estruturas espaciais e temporais por meio de Visão Computacional

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