IARet: A Lightweight Multiscale Infrared Aerocraft Recognition Algorithm

Jiang, Xinhao; Cai, Wei; Yang, Zhiyong; Xu, Peiwei; Jiang, Bo

doi:10.1007/s13369-021-06181-7

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…However, because of its cumbersome algorithm steps and slow calculations, researchers proposed a fast R-CNN [15] and a faster R-CNN [16] to improve precision and reduce calculation speed. At present, two-stage algorithms are widely applied in fields such as unmanned driving, military detection [17,18], facial recognition, and industrial detection, yielding good results.…”

Section: Related Workmentioning

confidence: 99%

Few-Shot Air Object Detection Network

Cai,

Wang,

Jiang

et al. 2023

Electronics

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Focusing on the problem of low detection precision caused by the few-shot and multi-scale characteristics of air objects, we propose a few-shot air object detection network (FADNet). We first use a transformer as the backbone network of the model and then build a multi-scale attention mechanism (MAM) to deeply fuse the W- and H-dimension features extracted from the channel dimension and the local and global features extracted from the spatial dimension with the object features to improve the network’s performance when detecting air objects. Second, the neck network is innovated based on the path aggregation network (PANet), resulting in an improved path aggregation network (IPANet). Our proposed network reduces the information lost during feature transfer by introducing a jump connection, utilizes sparse connection convolution, strengthens feature extraction abilities at all scales, and improves the discriminative properties of air object features at all scales. Finally, we propose a multi-scale regional proposal network (MRPN) that can establish multiple RPNs based on the scale types of the output features, utilizing adaptive convolutions to effectively extract object features at each scale and enhancing the ability to process multi-scale information. The experimental results showed that our proposed method exhibits good performance and generalization, especially in the 1-, 2-, 3-, 5-, and 10-shot experiments, with average accuracies of 33.2%, 36.8%, 43.3%, 47.2%, and 60.4%, respectively. The FADNet solves the problems posed by the few-shot characteristics and multi-scale characteristics of air objects, as well as improving the detection capabilities of the air object detection model.

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Section: Related Workmentioning

confidence: 99%

Few-Shot Air Object Detection Network

Cai,

Wang,

Jiang

et al. 2023

Electronics

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“…This infrared recognition method can achieve 88.69% recognition accuracy and 50 FPS speed. The IARet [ 16 ] performs well in single infrared image object detection, and the Focus module is designed to improve the detection speed. The IARet is also lightweight, with the entire model measuring just 4.8 MB.…”

Section: Introductionmentioning

confidence: 99%

Dual-YOLO Architecture from Infrared and Visible Images for Object Detection

Bao

Cao

Hao

et al. 2023

Sensors

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With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.

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“…In addition to its military value, camouflaged object detection (COD) can be applied to industrial detection (e.g., equipment defect detection [4]), medical diagnoses (e.g., testing whether lungs are infected by pneumonia [5,6]), monitoring and protection (e.g., suspicious person or unmanned aerial vehicle intrusion detection [7,8]) and unmanned driving (e.g., road obstacle detection [9]).…”

Section: Introductionmentioning

confidence: 99%

MAGNet: A camouflaged object detection network simulating the observation effect of a magnifier

Jiang

Cai

Jiang

et al. 2022

Preprint

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In recent years, protecting important objects by simulating animal camouflage has been widely used in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult than traditional object detection techniques because of the high degree of fusion of camouflaged objects with the background. In this paper, we strive to more accurately identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier, called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules, i.e., the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a region. The two sets of output camouflaged object maps are merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, is designed based on two modules to give higher attention to the camouflaged object. Extensive experiments demonstrate that compared with 14 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics on the public COD dataset, and the segmentation accuracy is significantly improved. We also validate the models' generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explore some extended applications of COD.

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IARet: A Lightweight Multiscale Infrared Aerocraft Recognition Algorithm

Cited by 9 publications

References 23 publications

Few-Shot Air Object Detection Network

Few-Shot Air Object Detection Network

Dual-YOLO Architecture from Infrared and Visible Images for Object Detection

MAGNet: A camouflaged object detection network simulating the observation effect of a magnifier

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