A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm

Moradi, Saed; Moallem, Payman; Sabahi, Mohamad Farzan

doi:10.1016/j.infrared.2018.01.032

Cited by 58 publications

(35 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a non-partisan comparison, all methods were measured on the ICCV 2019 ISOS Single dataset only. The other group (handcraft-based) consisted of IPI [6], MPCM [32], PSTNN [37], MoG [62], RIPT [36], FKRW [33], NRAM [38], AAGD [34], Top-hat [13], NIPPS [35], LSM [19], and Max-median [21].…”

Section: Methods In Comparisonmentioning

confidence: 99%

“…In particular, DataLossGAN [22] and Asymmetric Contextual Modulation (ACM) [31] have obtained magnificent performance on their datasets. Meanwhile, handcraft-based methods [6,20,21,[32][33][34][35][36][37][38] have also been studied. Researchers have analyzed local properties in scenes to distinguish the foreground from the background, patch by patch, and have applied a few well-designed filters to whole images in a sliding window manner.…”

Section: Related Workmentioning

confidence: 99%

“…The first is background suppression; the second is intensification of the objects of interest. Most traditional approaches [6,20,21,[32][33][34][35][36][37][38] execute the two objectives sequentially with only a few filters, which are elicited through human analysis. CNN-based methods perform the whole task under latent space comprised of numerous parameters in the network regardless of the sequence.…”

Section: Deeper Network Vs Vanishing Gradientmentioning

confidence: 99%

See 2 more Smart Citations

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

Song

Kim

2021

Remote Sensing

View full text Add to dashboard Cite

Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%).

show abstract

Section: Methods In Comparisonmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Deeper Network Vs Vanishing Gradientmentioning

confidence: 99%

See 1 more Smart Citation

AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

Song

Kim

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The sub-patch size d determines the dimension of the local contrast element c, and thus indirectly determines the validity of the seeds selection map. Additionally, d is involved in constructing the modified Laplacian matrix (see Equation (13)). The weight constant λ is mainly used for seeds selection and target segmentation.…”

Section: Sensitivity Analysis Of Crucial Parametersmentioning

confidence: 99%

“…There are numerous methods for small-target detection problems, and these algorithms can be roughly categorized into two representative groups: track before detection (TBD) [10][11][12] and detection before track (DBT) [13][14][15]. Prior knowledge of the targets and backgrounds are necessarily required and have strong influence for the TBD methods.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection via Modified Random Walks

Xia

Zhao

2018

Remote Sensing

View full text Add to dashboard Cite

Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in both target enhancement and detection performance.

show abstract