IR ship target saliency detection based on lightweight non-local depth features

Liu, Zhaoying; Jiang, Tianpeng; Zhang, Ting; Li, Yujian

doi:10.1109/eitce47263.2019.9095005

Cited by 2 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visible Chen [12], Cane [13], Marie [14], Chen [15], Chen [16], Liu [17], Shan [18], Gal [19], Lin [20], Chen [21], Lee [22], Feng [23], Shan [24], Fefilatyev [25] IR N/A Tang [26], Liu [27], Hu [28], Lin [29] MWIR Özertem [30], Wang [31] LWIR Sun [32], Lu [33], Bai [34], Leira [35], Bai [36], Mumtaz [37], Singh [38], Zhang [39], Xu [40], Zhou [41], Schöller [42], Li [43], Westlake [44] Visible + IR N/A Islam [45], Wei [46] Visible + MWIR Nita [47] Visible + LWIR Zhang [48], Ribeiro [49], Farahnakian [50] Visible + SWIR + LWIR Stets [51] Visible + SWIR + MWIR + LWIR Bouma [52] Visible: refers to visible-band images; IR: infrared images, including short-wave infrared (SWIR), medium-wave infrared (MWIR), and long-wave infrared (LWIR); N/A, means that the type of image was not specified in the paper. from the statistical data that infrared images are used the most, followed by visible images.…”

Section: Types Of Electro-optical Images Articlesmentioning

confidence: 99%

“…Li [43] used intensity and contrast features. Liu [27] proposed an improved light nonlocal depth feature (NLDF) for low-contrast infrared images. Mumtaz [37] used graph-based visual saliency (GBVS).…”

Section: Saliency Detectionmentioning

confidence: 99%

“…Li [43], Liu [27], Mumtaz [37], Lin [20] (1) Suitable for thermal infrared images; (2) It works well for low-contrast images.…”

Section: Saliency Detectionmentioning

confidence: 99%

“…Chen [15], Shan [24] Other Others Gal [88], Farahnakian [50], Liu [27], Cane [13], Nita [47] Many researchers have used one-stage detectors. In ship detection, the detection speed needs to be considered.…”

Section: One-stage and Two-stage Detectors In Ship Detectionmentioning

confidence: 99%

See 3 more Smart Citations

A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments

Wang

Fan

et al. 2021

JMSE

View full text Add to dashboard Cite

The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.

show abstract