In a military scenario, targets release decoy projectiles to achieve cover, then escape, evading the attack range of the guidance system. Therefore, recognizing target behaviors such as releasing decoys, escaping, and maneuvering, and locating the spatial position and behavior of the target can provide richer information for the precise targeting of the guidance system. Based on the behaviors of both the target and decoy, a behavioraware anti-interference algorithm is proposed. The core idea of this method is to link event recognition with spatio-temporal action localization tasks. It extracts spatio-temporal features of consecutive frames through a 3D backbone network and utilizes a 2D backbone network with a feature pyramid to extract spatial features at different levels of key frames for behavior detection at different scales. Additionally, a context extraction network is introduced in the key frames to achieve global context awareness, improving the accuracy of event recognition. Taking into account the correlation between spatial information, temporal information, and contextual information, this paper introduces channel attention mechanisms to fuse various feature components. Simultaneously, a non-local attention module is incorporated into the 3D backbone network to enhance the understanding of global content and the connection between different frames. Experimental results demonstrate that the proposed algorithm outperforms other algorithms, achieving a frame mean Average Precision (mAP) of 92.69% on our anti-interference dataset,enabling better resistance to interference.
In order to realize the rapid perception of complex scenes, the traditional scene complexity assessment algorithm has strong limitations in feature representation and scope of application, and it is difficult to deal with complex scenes. However, the existing deep network methods are lack of the consideration of the correlation between the underlying features of gray image and the complexity level, and the amount of parameters is too high to meet the needs of rapid response in practical applications. Based on the deep separable convolution module and residual connection structure, this paper designs a lightweight complexity assessment network X-CENet with stronger feature expression ability. A dense connection module which makes full use of multi-level features is introduced to improve the feature expression ability of the network for scene images. The underlying information such as image texture is particularly important for the assessment of complexity, so the feature cascade layer of the head and tail of the main modules is added to strengthen the utilization of the underlying feature information in the network. Experiments show that compared with other deep networks, this method can obtain higher assessment accuracy in the dimensions of image characteristics and detection performance with smaller parameters. Compared with the Inception V3 with similar parameter amount, this method improves the LCC index by 2.849% and the SRCC index by 3.338%.
The discrimination of interferences, especially artificial interferences, such as decoy, is crucial to improving the target’s detection performance. The differences in the kinematics characteristics between the target and the decoys are the main foundation to classify targets and kinds of decoys. The kinematics characteristics of the target and decoy usually represented by their behavior patterns. In this paper, learned from the human behavior recognition methods, a method for infrared target and decoy recognition based on the behavior recognition network was proposed. Our method combines detection network (Faster-RCNN), association algorithm (Deep-Sort), Inflated 3D convolutional network (I3D) with long-range attention block to perform target and decoy behavior recognition. Interactions with surrounding objects and other objects contain important information towards understanding the behavior. Improving the non-local attention mechanism by aggregating channel-wise attention and trajectory attention, our proposal method enables the I3D network to efficiently capture relation features on any positions, time and channels, especially trajectory behavior features of the target and decoy, that improve the discriminative ability of the anti-interference behavior recognition network. Experiments show our proposed method has a better performance than the original non-local attention network, achieve a state-of-the-art.
Single image dehazing is a challenging ill-posed restoration problem. Most of dehazing algorithms follow the classical atmospheric scattering model and adopt same parameters for different hazy density areas in hazy images. In this paper, we proposed an end-to-end dehazing algorithm, called Dehazing Network based on Haze Density(DNBHD). The proposed network involves a haze density map estimation network and a dehazing network. By the estimated haze density map, hazy image is divided into a mist region and a dense fog region which are respectively feed into dehazing network. Compared with previous dehazing algorithm, DNBHD is independent on the atmospheric scattering model, and considers uniform fog distribution in images. We use different parameters to handle different hazy density regions, avoiding color distortion and inappropriate brightness caused by overall defogging. The experiments show our algorithm achieves significant improvements over the state-of-the-art methods
Target detection is a very important and basic problem of computer vision and image processing. The most often case we meet in real world is a detection task for a moving-small target on moving platform. The commonly used methods, such as Registration-based suppression, can hardly achieve a desired result. To crack this hard nut, we introduce a Global-local registration based suppression method. Differ from the traditional ones, the proposed Global-local Registration Strategy consider both the global consistency and the local diversity of the background, obtain a better performance than normal background suppression methods. In this paper, we first discussed the features about the small-moving target detection on unstable platform. Then we introduced a new strategy and conducted an experiment to confirm its noisy stability. In the end, we confirmed the background suppression method based on global-local registration strategy has a better perform in moving target detection on moving platform.
In this paper, a new target reconstruction method considering the atmospheric refraction is presented to improve 3D reconstruction accuracy in long rang surveillance system. The basic idea of the method is that the atmosphere between the camera and the target is partitioned into several thin layers radially in which the density is regarded as uniform; Then the reverse tracking of the light propagation path from sensor to target was carried by applying Snell’s law at the interface between layers; and finally the average of the tracked target’s positions from different cameras is regarded as the reconstructed position. The reconstruction experiments were carried, and the experiment results showed that the new method have much better reconstruction accuracy than the traditional stereoscopic reconstruction method.
This paper presents a method for ship target detecting in complex background. It aims at solving two difficulties in
detection. The first one is that the ships docking in-shore cannot be segmented because of its gray level similarity to land,
and the second is that the ships linked side by side cannot be easily located as separate correct target. The first one is
solved by extracting water region firstly by measure of harbor-template matching. In order to reduce the impact of angle
difference which leads to error, we update the template by the corresponding angle calculated recur to line feature. Then
matching fine with the updated template to extract water region wholly in which the segment is effective. For the second
difficulty, the smallest minimum bounding rectangle (SMBR) of the segmented areas are obtained by contour tracing,
and the areas are projected to the two different directions of its SMBR, then the projection curves are acquired. If the
ships are linking together, the peak-valley-peak pattern will exist in the projection curve and the valley-point indicates
the ships' connection position. Then the ships can be separated by cutting the area at connection position along the
projection direction. The experiment results verify the efficiency and accuracy of our method.
The new hit-aim positioning algorithm based on template matching is proposed, which aim at airport hit-aim location
problem in the case of that the airport presents in infrared images partially. In the algorithm, the homogeneity
transformation based on co-occurrence matrices was brought forward to enlarge the difference between the airfield
runway and background firstly. After the transformed image being segmented, morphologic filter was applied to the
segmented binary image to improve airfield runway completeness by filling the small hole in the runway region, and to
suppress background clutter by clearing up the remnant fragment in the background area. Later, the multiple templates
matching method, in which the distribution pattern among the templates was utilized to improve the matching reliability
and positioning precision, was performed to match the weighted structure templates with the extracted binary image. The
hit-aim was able to be located indirectly with relatively position relation between the hit-aim and templates. The
preparation method for weighted structure templates based on the satellite visible image was also given. The experiment
shows that the proposed algorithm can achieved high matching rate, and have rather robustness to scale and rotation.
An adaptive tracking method for ground target in FLIR image including centric and eccentric tracking is presented in this
paper. The eccentric tracking is adopted to assist ATR or manual target acquisition by stabilizing the light axis of the
seeker. The tracking adaptability detection at the start of centric tracking avoids tracking bad locked point and decreases
the shift, slide and jump in the subsequent tracking. Combination of periodic template-updating based on scale variance
rate and interruptive template-updating based on supervision of tracking point enhances the adaptability of tracking
template. Supervision, modification of current tracking point and the combination of direct and indirect tracking increase
the stability of tracking system. The multi-scale and size-variable template is adopted to fit the variable target scale. The
detection and adoption of occlusion mask increase the accuracy of the tracking system. The hardware system based on
multi-DSP takes a real-time parallel processing. The trial results show that this method is efficient to reduce the
possibility of shift, slide, and jump in ground target tracking comparing with the traditional correlation tracking method.
The aim of the present work is to propose a brand-new algorithm based on an adaptive double threshold nonlinear anisotropic diffusion equation (DTPDE) to detect and track moving dim targets against complex cluttered background in infrared (IR) image sequences. We also illustrate the performance comparisons of the proposed algorithm DTPDE and two-dimensional least mean squares (TDLMS) on real IR image sequence data. Extensive experiment results demonstrate the proposed novel algorithm's flexibility and adaptability in detecting moving weak dim targets.
A new matching measure based on the distance transform weighted by the object contour's characteristic is proposed to enhance the matching contribution of the local feature. In this paper, the characteristic of the contour is expressed with corner membership, and the distance transform is weighted by the difference of the corner membership between the object contour and model, and a more robust matching measure is obtained. The real forward looking infrared (FLIR) images matching experiment shown that the proposed matching measure increase the class-between distance between the object and non-object remarkably, and improve the matching probability and performance.
Radar scene matching technique has been widely found in many application fields such as remote sensing, navigation, terrain-map match, scenery variance analysis and so on. Radar image geometry is quite different from that of optical satellite imagery, whose imaging is a slanting imaging of electromagnetic microwave reflection. The different characters between radar image and optical satellite images are very distinct, such as the layover distortion of ground-truth and speckle noise, which degrades the image to such an extent that the features are very unclear and difficult to be extracted. So the factors such as the hypsography, ground truth, sensor altitude and imaging time should be taken into account for radar image and optical image matching. In this paper, we develop an image match algorithm based on reference map multi-area selection using fuzzy sets. Image matching is generally a procedure that calculates the similarity measurement between sensed image and the corresponding intercepted image in reference map and it searches the maximum position in the correlation map. Our method adopts a converse matching strategy which selects multi-areas in optical reference map using fuzzy sets as model images, then match them on the sensed image respectively by normalized cross correlation matching algorithm and fuse the match results to get the optimum registered position. Multi-areas selection mainly considers two influence factors such as ground-truth texture features and the hypsography (DEM) of imaging region, which will suppress the influence of great variance imaging region. Experiment results show the method is effective in registering performance and reducing the calculation.
Geological structures used to be faint and blur in remotely sensed image as they are usually buried or hidden under the ground. In spite of that, the information of them can be found out in single band or multi-bands of the multi-spectrum data. Geological structures can be considered as image anomalies upon complex background. It is an important approach for geological structure enhancement to enhance the difference between the anomaly and its background in single band or multi-bands. Characteristics of spatial spectral distribution is thus special significant for image processing. Along this way, we improved on two methods, mean-residue (MR) and selective principal component analysis (SPCA), with emphasis on spatial spectral analysis, to enhance geological structures. Applications of the methods to actual TM data have arrived at good results. The keys of the two methods are respectively the determination of filter kernel and the selection of band pair.
In the model-based recognition methods, the result's confidence is decided by the feature distance between the segmented region and the target model, and can be defined as the posterior probability that can be computed from the object and background's prior probability and conditional probability with Bayesian formula. However, when recognizing the target, many physical constrains, or image measurements of object region and background region, can be applied on the validation of the recognition result, and should be introduced into the confidence analysis. In this paper, we proposed a new method to analyze the target recognition quality by combining the physical constrains or prior knowledge into confidence analysis within the frame of mathematical statistic theory and Dempster-Shafer's evidence theory. In this method, the usability of the information sources is appraised with Kolmogorov-Smirnov test method and the different computation models to compute the belief value to classifier's result corresponding to the different information source types were also proposed. The method was tested on the real sequences of images, and the result indicated that the proposed method for confidence analysis is feasible and effective.
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