Inshore Ship Detection in Remote Sensing Images via Weighted Pose Voting

Arbitrary-Oriented Inshore Ship Detection based on Multi-Scale Feature Fusion and Contextual Pooling on Rotation Region Proposals

“…Inshore ship detection with traditional methods usually focuses on special characteristics of ships to design features and recognize targets. He et al, adopt weighted voting and rotation-scale-invariant pose to detect ships [12], and Bi et al [8] employ an omnidirectional intersected two-dimension scanning strategy and decision mixture model to detect ships. Since artificially designed features cannot cope with complex port background, as a result, more and more deep learning methods are proposed in the inshore ship detection task during the most recent three years while non-deep-learning ones are seldom presented.…”

“…Moreover, inshore ships in very high-resolution images exhibit various sizes and shapes, and some of them are densely arranged near the coast. As a result, the detection of inshore ships is still a challenging task [12].…”

FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition

“…Next, Otsu method [35] is performed to obtain a second type of segmentation mask M2 which maximizes inter-class variance in the logarithmic domain and complex domain respectively. Last, on top of the Otsu result, the C-V model [40] is further conducted to adjust the M2 mask and finally obtain the third mask M3. Finally, a majority voting mechanism is applied to merge M1, M2 and M3 to reach the final segmentation mask.…”

Vessel detection and classification from spaceborne optical images: A literature survey

“…vessels). Some papers deal with “inshore vessel detection” in ports and in inland waters (Beşbinar and Alatan, 2015, He et al, 2017, Hu et al, 2015, Li et al, 2016, Li et al, 2016, Liu et al, 2014, Ren et al, 2015, Xu et al, 2014), which, as opposed to vessel detection on open sea, has to cope with high similarity between vessels and port background in terms of colour and structure (Beşbinar and Alatan, 2015). The articles that developed algorithms not only for vessel detection but also for other geospatial object detection (such as airplanes, cars, storage tanks, baseball diamonds, bridges, etc.)…”

“…Zhang et al2016Google Earthn/a0.12, 0.25−n/aDeep learning method: ship proposal extraction convolution neural networks−yesVessel detectionR. Zhang et al2016GaoFen-1, VRRS-1, Google EarthPAN2 (GF-1), 16 (VRSS-1)−20 pixelsDeep learning method: convolutional neural network, Singular value decomposition algorithmDiscrimination: SVM classifieryesVessel detectionZou and Shi2017Google Earthn/a−−n/aComputer vision method: rotation and scale-invariant method based on the pose consistency voting−yesInshore vessel detectionHe et al2017Google EarthPAN2−n/aSalient-based method: maximum symmetric surround method, cellular automata dynamic evolution model, Otsu algorithmDiscrimination: histogram of oriented gradient, AdaBoost classifieryesVessel detectionWang et al2017Google EarthB, G, R−−10 pixelsSalient-based method: combined saliency map model through a self-adaptive threshold based on Entropy informationDiscrimination: based on gradient featuresyesVessel detectionXu et al2017Google EarthPAN2−n/aSalient-based method: Histogram-based contrast method, phase spectrum of a Fourier transform, surface regular indexDiscrimination: Simple shape analysis, structure-local binary pattern, AdaBoost algorithmyesVessel detectionYang et al…”