DRUNet: A Method for Infrared Point Target Detection

Wei, Chang'an; Xu, Jianzhong; Yang, Jingli; Jiang, Shouda

doi:10.3390/app12189299

Cited by 2 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te concatenated features are regarded as the inputs of the following decoder. Compared with FCN, U-Net only needs one training process to simulate the specifc and spatial information [44,45]. Chen et al [46][47][48][49] designed the DeepLab models for semantic segmentation, which chose the dilated convolution as a substitute for the downsampling operator.…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Guided Model Fusion Network with Attention Mechanism for Infrared Small Target Segmentation

Zhang

Chen

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Infrared small target segmentation plays an important role in infrared guidance systems. In this paper, a fine-grained guided model fusion network with attention mechanism (FAMNet) is proposed for improving the performance of the infrared small target segmentation. An autonomous traditional feature extraction algorithm based on information entropy and four-directional gradient contrast is proposed for solving the difficulty of feature extraction from small targets. The deep features are extracted from an improved U-Net. The two kinds of features are fused by channel shuffle, which could enhance the communication efficiency between the channels of the two features. More context information from the target and background is introduced into the network by cross layer parallelization convolutional block attention module (CLPCBAM). Compared with some state-of-the-art techniques, the proposed FAMNet performs significantly better in terms of IoU, nIoU, and F1-score comprehensively.

show abstract

Section: Related Workmentioning

confidence: 99%

Fine-Grained Guided Model Fusion Network with Attention Mechanism for Infrared Small Target Segmentation

Zhang

Chen

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…The first factor is the large size of the convolutional step in the convolutional neural network. Target detection algorithms use convolutional neural networks as feature extraction tools; the feature map of the target continues to shrink during the convolution of the network [18,19], and the convolution step length is likely to be larger than the infrared small target size, which makes it difficult to transfer infrared small target features to the deep network [20][21][22]. The second factor is that the distribution of the dataset is not ideal.…”

Section: Introductionmentioning

confidence: 99%

IRSDD-YOLOv5: Focusing on the Infrared Detection of Small Drones

Yuan

Sun

Zuo

et al. 2023

Drones

View full text Add to dashboard Cite

With the rapid growth of the global drone market, a variety of small drones have posed a certain threat to public safety. Therefore, we need to detect small drones in a timely manner so as to take effective countermeasures. At present, the method based on deep learning has made a great breakthrough in the field of target detection, but it is not good at detecting small drones. In order to solve the above problems, we proposed the IRSDD-YOLOv5 model, which is based on the current advanced detector YOLOv5. Firstly, in the feature extraction stage, we designed an infrared small target detection module (IRSTDM) suitable for the infrared recognition of small drones, which extracted and retained the target details to allow IRSDD-YOLOv5 to effectively detect small targets. Secondly, in the target prediction stage, we used the small target prediction head (PH) to complete the prediction of the prior information output via the infrared small target detection module (IRSTDM). We optimized the loss function by calculating the distance between the true box and the predicted box to improve the detection performance of the algorithm. In addition, we constructed a single-frame infrared drone detection dataset (SIDD), annotated at pixel level, and published an SIDD dataset publicly. According to some real scenes of drone invasion, we divided four scenes in the dataset: the city, sky, mountain and sea. We used mainstream instance segmentation algorithms (Blendmask, BoxInst, etc.) to train and evaluate the performances of the four parts of the dataset, respectively. The experimental results show that the proposed algorithm demonstrates good performance. The AP50 measurements of IRSDD-YOLOv5 in the mountain scene and ocean scene reached peak values of 79.8% and 93.4%, respectively, which are increases of 3.8% and 4% compared with YOLOv5. We also made a theoretical analysis of the detection accuracy of different scenarios in the dataset.

show abstract

DRUNet: A Method for Infrared Point Target Detection

Cited by 2 publications

References 24 publications

Fine-Grained Guided Model Fusion Network with Attention Mechanism for Infrared Small Target Segmentation

Fine-Grained Guided Model Fusion Network with Attention Mechanism for Infrared Small Target Segmentation

IRSDD-YOLOv5: Focusing on the Infrared Detection of Small Drones

Contact Info

Product

Resources

About