Detection of sea surface targets in large-scale remote sensing images is one of the important research topics of ocean remote sensing technology. Ocean remote sensing images have the characteristics of wide format, strong interference and small target. This paper adopts the spinning target detection method, and proposes a ship detection model based on YOLO to output the real length, width and axial information. The model can accurately output the position, length and width and axial information of a ship target by predicting the minimum external rectangular area of the ship target, so as to realize multi-target detection and improve the detection performance significantly. To improve the recall rate of the target detection algorithm, this paper adopts the spinning target detection method, and proposes a ship detection model based on YOLO. Through redefining the representation of the rotation matrix and redesigning a new network loss function and the rotated IOU computing method, this model accurately outputs the real length, width and axial information, increases the output feature dimensions, and effectively raises the recall rate and speed of multi-target detection. Lastly, to improve the practicability of the algorithm on mobile devices, the model is processed in a lightweight way. Its parameters are significantly reduced while the detection accuracy is ensured.
This paper presents an approach for tracking airborne target against oppressive infrared decoys. Oppressive decoy lures
infrared guided missile by its high infrared radiation. Traditional tracking algorithms have degraded stability even come
to tracking failure when airborne target continuously throw out many decoys. The proposed approach first determines an
adaptive tracking window. The center of the tracking window is set at a predicted target position which is computed
based on uniform motion model. Different strategies are applied for determination of tracking window size according to
target state. The image within tracking window is segmented and multi features of candidate targets are extracted. The
most similar candidate target is associated to the tracking target by using a decision function, which calculates a
weighted sum of normalized feature differences between two comparable targets. Integrated intensity ratio of association
target and tracking target, and target centroid are examined to estimate target state in the presence of decoys. The
tracking ability and robustness of proposed approach has been validated by processing available real-world and
simulated infrared image sequences containing airborne targets and oppressive decoys.
KEYWORDS: Image filtering, Target detection, Detection and tracking algorithms, Image processing, Digital filtering, Optical filters, Infrared imaging, Image processing algorithms and systems, Signal to noise ratio, Signal processing
A crucial problem in Infrared Search and Track (IRST) systems is the detection of moving point targets, there are many
algorithms reported in the literature for dealing with this problem, yet none of them yield acceptable results under all
situations. In this paper, we describe a new temporal variance filter (TVF) for detecting targets whose velocity are higher
than 1 pixel/frame; the filter iteratively estimates the temporal variance of each pixel, then subtracts the last iteration step
variance from the variance of current step. Subsequently, we introduce a novel image segmentation algorithm in order to
extract point targets from clutter background, the trajectories of the point targets could be established by post-processing
algorithm. Before applying the temporal filter, the anti max-median filter given by Suyog D. Deshpande et al. is
incorporated as a preprocessing technique to suppress cloud clutter. It is assumed that targets' velocity is higher than 1
pixel/frame; targets with sub-pixel/frame velocity are not considered. The performance of our approach is evaluated by
using available real-world infrared image sequences containing simulated moving point targets; it performs steadily
under most situations.
We assess the performance of a novel three-dimensional double directional filtering (3DDDF) algorithm for detecting and tracking weak moving dim targets against a complex cluttered background in infrared (IR) image sequences. This proposed method increases the target energy accumulation ability further than the three-dimensional directional filter (3DDF) method. Prior to the filtering, a new prewhitening method termed a three-dimensional spatialtemporal adaptive prediction filter (TDSTAPF) is used to suppress the cluttered background. Extensive experiment results demonstrate the proposed algorithms' ability to detect weak dim point targets against a complex cloud-cluttered background in real IR image sequence and the performance comparisons of the proposed method and 3DDF.
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