Localization and classification of space objects using EfficientDet detector for space situational awareness

AlDahoul, Nouar; Karim, Hezerul Abdul; Castro, Angelo De; Tan, Myles Joshua Toledo

doi:10.1038/s41598-022-25859-y

Cited by 8 publications

(5 citation statements)

References 37 publications

(69 reference statements)

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“…Hence, we intend to enhance the performance of recognition by using object detection method after image fusion method. We expect that this can improve the detection performance in comparison with previous works that targeted RGB images only for detection purposes [81]. Additionally, an ensemble of object detectors may also contribute to enhance the detection performance in general and the recognition specifically.…”

Section: Discussionmentioning

confidence: 91%

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

et al. 2023

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Space situational awareness (SSA) system requires recognition of space objects that are varied in sizes, shapes, and types. The space images are challenging because of several factors such as illumination and noise and thus make the recognition task complex. Image fusion is an important area in image processing for various applications including RGB-D sensor fusion, remote sensing, medical diagnostics, and infrared and visible image fusion. Recently, various image fusion algorithms have been developed and they showed a superior performance to explore more information that are not available in single images. In this paper, we compared various methods of RGB and Depth image fusion for space object classification task. The experiments were carried out, and the performance was evaluated using 13 fusion performance metrics. It was found that the guided filter context enhancement (GFCE) outperformed other image fusion methods in terms of average gradient (8.2593), spatial frequency (28.4114), and entropy (6.9486). additionally, due to its ability to balance between good performance and inference speed (11.41 second), GFCE was selected for RGB and Depth image fusion stage before feature extraction and classification stage. The outcome of fusion method is fused images that were used to train a deep ensemble of CoAtNets to classify space objects into ten categories. The deep ensemble learning methods including bagging, boosting, and stacking were trained and evaluated for classification purposes. It was found that combination of fusion and stacking was able to improve classification accuracy largely compared to the baseline methods by producing an average accuracy of 89 % and average F1 score of 89 %.

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Section: Discussionmentioning

confidence: 91%

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

et al. 2023

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“…To achieve optimal model performance with limited computing resources, researchers typically use EfficientNet as the backbone network for EfficientDet [45,46]. However,…”

Section: A Global-local Se Modulementioning

confidence: 99%

Fuzzy EfficientDet: An Approach for Precise Detection of Larch Infestation Severity in UAV Imagery Under Dynamic Environmental Conditions

Yang,

Li,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, a novel deep learning framework, fuzzy EfficientDet, is proposed to address the challenge of accurately detecting larch infested by Coleophora laricella pests in UAV imagery, where the key innovation is the incorporation of the fuzzy spatial attention mechanism (FSAM), which can effectively deal with the problem of model uncertainty due to the complexity of environmental transformations and image features. First, this study designs and implements a Global-Local Squeeze-and-Excitation Module, which profoundly integrates global and local feature information, realizes the dynamic adaptation of the importance of feature channels in the EfficientNet, and thus improves the overall feature expression efficiency of the network. Second, this study constructed a dense Bi-FPN architecture, which adds a dense connection structure to the original Bi-FPN to enhance the modeling accuracy for small targets and long-range spatial dependencies. Finally, this study develops the fuzzy spatial attention mechanism (FSAM), which can effectively mitigate the unstable performance of the EfficientDet in the face of image feature fluctuations triggered by changes in lighting conditions and seasonal effects. Experiments demonstrate that the proposed fuzzy EfficientDet model shows superior performance compared to the traditional SSD, Faster R-CNN, YOLO V5, and the unimproved EfficientDet target detection method on the Swedish Forest Agency (2021) dataset, with its mAP as high as 94.29%. This result demonstrates that fuzzy EfficientDet provides an efficient and reliable solution when dealing with the task of target detection in UAV images, especially in dealing with environmental uncertainty and complex feature extraction.

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“…The EfficientDet model is used to detect the objects (i.e., vehicles) in the RSI. For combining features with a top-down direction, a conventional approach, named Feature pyramid network (FPN), was used [19]. The PANet (path aggregation network) allows for the forward and reverse flows of feature fusion from low to high resolution.…”

Section: Stage I: Object Detectormentioning

confidence: 99%

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

Alajmi,

Alamro,

Al-Mutiri

et al. 2023

Remote Sensing

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Remote sensing imagery involves capturing and examining details about the Earth’s surface from a distance, often using satellites, drones, or other aerial platforms. It offers useful data with which to monitor and understand different phenomena on Earth. Vehicle detection and classification play a crucial role in various applications, including traffic monitoring, urban planning, and environmental analysis. Deep learning, specifically convolutional neural networks (CNNs), has revolutionized vehicle detection in remote sensing. This study designs an improved Chimp optimization algorithm with a DL-based vehicle detection and classification (ICOA-DLVDC) technique on RSI. The presented ICOA-DLVDC technique involves two phases: object detection and classification. For vehicle detection, the ICOA-DLVDC technique applies the EfficientDet model. Next, the detected objects can be classified by using the sparse autoencoder (SAE) model. To optimize the SAE’s hyperparameters effectively, we introduce an ICOA which streamlines the parameter tuning process, accelerating convergence and enhancing the overall performance of the SAE classifier. An extensive set of experiments has been conducted to highlight the improved vehicle classification outcomes of the ICOA-DLVDC technique. The simulation values demonstrated the remarkable performance of the ICOA-DLVDC approach compared to other recent techniques, with a maximum accuracy of 99.70% and 99.50% on the VEDAI dataset and ISPRS Postdam dataset, respectively.

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Localization and classification of space objects using EfficientDet detector for space situational awareness

Cited by 8 publications

References 37 publications

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

Space Object Recognition With Stacking of CoAtNets Using Fusion of RGB and Depth Images

Fuzzy EfficientDet: An Approach for Precise Detection of Larch Infestation Severity in UAV Imagery Under Dynamic Environmental Conditions

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

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