291results about How to "Improve perception" patented technology

The invention discloses a short-term wind power prediction method and system based on a dynamic graph neural network, and belongs to the field of wind power prediction. Dividing a power sequence into a high-frequency fluctuation set and a low-frequency stationary set by using VMD-CEEMDAN joint decomposition and SSA clustering optimization; a DGAT module is adopted to fuse geography, data and learnable prior, a local sub-graph is dynamically constructed to extract spatial features, meanwhile, a multi-scale convolution block is utilized to parallelly capture high-frequency mutation and low-frequency trends through an MS-TCN module, multi-scale time features are generated, time feature fusion is guided through space and geography combined representation, and time feature fusion is realized. Collaborative modeling of multi-source heterogeneous data is achieved, finally fusion features are input into a full-connection layer to predict fluctuation set prediction power, the prediction power is combined with stationary set prediction power, and a short-term wind power prediction value is output; according to the method, the modeling capability of wind power plant complex association is remarkably improved, and reliable support is provided for real-time scheduling of a power grid.

The invention belongs to the technical field of medical image processing, and particularly relates to a linear Transform general focus recognition method based on multiple perception and context guidance, and the method comprises the steps: extracting the multi-scale features of a medical CT image through a backbone network, obtaining the edge gradient features in parallel, making up the missing of focus boundary information through an edge perception feature enhancement module, and carrying out the recognition of the focus. And then global feature modeling under linear complexity is realized through a polarity perception feature interaction module, a high-discrimination-force multi-scale feature map is generated by using a context-guided feature pyramid network, and finally the model is optimized by combining a Hungary algorithm and a joint loss function based on an end-to-end detection architecture of set prediction. The problems that the focus boundary is fuzzy, feature interaction and calculation efficiency are balanced, and the focus and background separation degree is weak are effectively solved, double improvement of calculation efficiency and detection precision on massive medical image data is achieved, and reliable support is provided for clinical precise auxiliary diagnosis.

The invention relates to the technical field of semantic segmentation, in particular to a night semantic segmentation method based on low illumination enhancement and edge optimization, and the method comprises the steps: inputting an image into a low-light enhancement repair network based on the Retinex theory, local contrast enhancement and adaptive feature fusion, obtaining a denoised and enhanced intermediate image, and carrying out the edge optimization of the intermediate image; inputting the intermediate image into a semantic segmentation network to obtain a category distribution diagram of each pixel; inputting a discriminator embedded with a channel attention module according to the category distribution diagram of each pixel, and optimizing a generator composed of a low light enhancement repair network and a semantic segmentation network through a multi-task joint optimization loss function; and inputting a night image to be detected and segmented into the optimized generator, and outputting a segmentation result. By adopting the method, the low-light enhancement repair network is combined with a local contrast enhancement and channel feature fusion mechanism, so that the overall brightness of the image is improved, the details and edge structures of the image are reserved, and the perception capability and robustness of the model are improved.

The invention provides an intelligent interpretation method for a dual-temporal remote sensing image, which is characterized in that a boundary constraint change detection model BCnet based on a visual basic model is constructed by the method, the universal semantic representation potential of the visual basic model is fully excavated, and a difference detail enhancement module and a multi-scale edge enhancement module are introduced, so that the visual basic model can be fully interpreted. The problem that high-frequency information is lost in the direct migration process of the visual basic model is solved, and fine description of tiny changes and complex textures is achieved. On the basis, an edge feature constraint strategy is designed, an edge feature aggregation module is combined, boundary supervision signals are introduced into a feature space and a prediction space at the same time, and the continuity and geometric integrity of a change detection result in the space structure are enhanced. Experimental results prove that the BCnet has the advantages of high robustness and precision in high-difficulty scenes such as dense building groups and complex edges.

The invention discloses an equipment fault prediction method and system, and belongs to the technical field of electric data processing. The method comprises the following steps: inputting state information of each component of equipment into a conditional generative adversarial network to generate time series data, and constructing a plurality of target maps with adjacency relations; time features and local graph features are extracted by using multi-branch convolution and a graph convolution structure of a convolution auto-encoder, and target graph features are obtained through splicing; performing channel weighting through a multi-scale attention module to obtain multi-scale image features; calculating cross attention weights among different target images and fusing the cross attention weights to obtain fused image features; and compressing the potential feature representation as a health index, constructing a health index time sequence, inputting the health index time sequence into a fault prediction model, and obtaining an estimated residual life of the equipment. According to the method, strong noise interference can be effectively filtered out, the problem of feature redundancy is solved, key degradation information is captured robustly, the equipment health state is accurately represented, and the residual life prediction precision is remarkably improved.

The invention relates to the technical field of path planning, in particular to a cable path planning method based on deep learning and multi-objective optimization, and the method comprises the following steps: based on remote sensing image data, employing a multi-scale semantic segmentation deep learning model, combining with an auxiliary accessibility tagging sample, and recognizing the type of each ground feature; constructing a ground feature accessible confidence field with continuous spatial distribution; constructing a confidence coupling path cost function, endowing a penalty weight to an area with low ground feature confidence, endowing a passage reward weight to an area with high confidence, and generating a confidence coupling path cost function for path search; and constructing an improved heuristic function, and executing path search by adopting a multi-objective optimization algorithm to obtain a cable path scheme. According to the method, the historical total cost of the current node is considered, and the average confidence risk from the current node to the target direction is predicted, so that prospective avoidance is realized.

The invention discloses a hydroelectric generating set fault diagnosis data enhancement method based on a diffusion model and generative adversarial training, and belongs to the field of industrial equipment fault diagnosis. According to the method, the data enhancement model combining the diffusion model and the generative adversarial network is constructed, a multi-modal non-Gaussian distribution mechanism and a latent variable control generation process are introduced, the limitation of a single Gaussian hypothesis of a traditional diffusion model is broken through, and richer and more real sample generation is realized. And a condition discriminator and a time sequence modeling mechanism are introduced, so that the training of the model under different noise levels is more stable, and the authenticity judgment capability of the discriminator on the generated sample is enhanced. The problems that an existing hydroelectric generating set fault diagnosis system is insufficient in sample, low in generated sample quality and unreal in sample distribution are solved, and the diversity and quality of data are remarkably improved.

The invention discloses a cloud edge end collaborative computing power scheduling system supporting task dynamic migration, which relates to the technical field of intelligent computing power scheduling and comprises a path modeling module, a conflict identification module, a time sequence checking module, a rehearsal switching module and an interference suppression module, and constructing a routing convergence time sequence profile and a link playback spectrum, modeling an overlapping window of the old path and the new path and a life cycle of a control signal, and generating a path evolution baseline for subsequent conflict identification. By means of path modeling, fingerprint identification, causal check, shadow rehearsal, interference suppression and the like, time sequence consistency of a control instruction and a data stream, continuous path switching and real-time extinguishing of link interference are realized, and the stability and reliability of cloud edge collaborative scheduling are improved.

The invention discloses a blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing images and deep learning and a medium, and relates to the technical field of information data processing. Combining meteorological data and water quality monitoring data to construct an adaptive dynamic environment algorithm to extract EXIF metadata including camera parameters and attitude angle information, and establishing a geometric projection model to perform coarse orthographic correction on an original aerial image; the method comprises the following steps: extracting a multi-scale cyanobacterial bloom image feature map by stages based on a ResNet architecture and in combination with a feature pyramid network FPN fused with an attention mechanism, and obtaining an instance mask of cyanobacterial bloom through an anchor-free region proposal network, ROI Align and a head network; based on an improved GIS space projection and deep learning algorithm, carrying out high-precision area measurement and calculation on a binary mask image converted from the instance mask; according to the method, the influence of irrelevant interference on blue-green algae identification can be reduced, and the accuracy and comparability of blue-green algae identification and area calculation are improved.

The invention discloses an intensive culture precise feeding control method based on fuzzy-MPC control, and relates to the technical field of aquaculture intelligent control, and the method comprises the steps: employing a sensing system, collecting multi-source sensing data including fish activity images, healthy growth states and water quality environment data in real time, and forming a comprehensive state data set; and based on the acquired multi-source sensing data, constructing an MTL-LSTM-SAT multi-task learning model. According to the method, the multi-task learning model fusing the LSTM and the soft attention mechanism is constructed, the long-term dependency relationship and dynamic association among the water quality, the feeding and the fish state can be captured, the attention mechanism enables the model to be automatically focused on a key time step, the perception ability of the water quality fluctuation and the feeding influence stage is enhanced, and the accuracy of the water quality fluctuation and feeding influence stage is improved. Meanwhile, double-task collaborative prediction of the growth speed and the disease proportion is achieved through the shared feature layer, a prospective state sequence of the future 7 days is output, and a quantitative and reliable prediction basis is provided for feeding decision making.

This invention proposes a water outlet device, comprising a main body with an inlet and an outlet; a drive unit rotatably disposed inside the main body; and an outlet unit rotatably disposed at the outlet of the main body, having one or more outlet holes, the outlet holes being inclined relative to the rotation axis of the outlet unit. Water flow from the inlet of the main body impacts the drive unit, causing it to rotate, which in turn drives the outlet unit to rotate. The arrangement of the one or more outlet holes on the outlet unit satisfies the following: a plane passing through the axis of the one or more outlet holes and perpendicular to the plane of rotation serves as the mounting surface of the outlet unit; any two adjacent mounting surfaces of the outlet units are parallel to each other or form an angle of 360° / N, where N is the number of outlet units. A showerhead incorporating the above-described water outlet device is also proposed. The water outlet device of this invention can achieve more aesthetically pleasing and diverse dynamic water sprays through the ingenious design of the outlet holes, thereby enhancing the massage effect.

An existing liver tumor automatic segmentation method is insufficient in expression in small focus, low-contrast edge and long-range space dependence modeling, and consequently high false positive and boundary deficiency are caused. Pure CNN is limited by a receptive field, pure Mama easily loses local details, multi-level attention stacking significantly increases parameter quantity, and traditional side supervision differential correction is difficult to accurately focus an uncertain area. The invention provides an end-to-end network, parallel ResNet and Mamba dual-coding and direct reaching a decoder after AFF fusion at the same scale, bottom features are accessed to a multi-scale feature fusion module to complete deep semantic enhancement, and a decoding side forms a GARS module concentration boundary difficult-to-distinguish pixel by matching an MSCB-EUCB-LGAG lightweight chain with four-stage Gaussian attenuation residual self-correction. Clinical level, the method can significantly reduce leak detection of small tumors, reduce false positive, and maintain geometric integrity of edges.

The present application relates to a kind of based on multi-modal big model to realize the method for interpretable offline signature authentication, comprising: constructing data set;Data set is preprocessed;Multi-modal big model is constructed;Wherein, multi-modal big model includes: visual Transformer submodel, big language submodel and local signature visual representation enhancement submodel;Using the data set after pre-processing, multi-modal big model is trained, and authentication model is obtained;Using authentication model, interpretable offline signature authentication is carried out.The present application can complete the authenticity identification of Chinese signature and english signature and give detailed explanation in natural language form.The authenticity identification accuracy of Chinese signature can reach more than 80%, and the authenticity identification accuracy of english signature can reach more than 90%.For different signature input, different test analysis report can be output, and different verification effect and analysis result can be obtained by adjusting reasoning parameter.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle target classification matching strike control method and system based on deep learning, and the method comprises the following steps: S1, collecting the original environment data of an unmanned aerial vehicle flight region, carrying out the preprocessing of the original environment data, and generating multi-mode perception data; and S2, inputting the multi-modal sensing data into a pre-constructed deep learning classification network, extracting multi-level depth features of the target through the deep learning classification network, carrying out classification identification on the target based on the multi-level depth features, and outputting target category information and target position information of the target. The multi-modal sensing data is input into the deep learning classification network for target recognition, the multi-modal fusion sensing mode can give full play to the complementary advantages of different sensors, high target recognition accuracy can still be kept in complex environments such as night, low illumination and severe weather, and the adaptive capacity of the system to environment changes is remarkably improved.

The invention discloses a biased binocular visual odometer method and device based on strawberry cuttlefish bionics. The biased binocular visual odometer method comprises the following steps: synchronously acquiring a bright field image and a dark field image collected by a biased binocular camera and inertial data of an inertial measurement unit; constructing a perceptual attention weight grid according to the bright field image and the dark field image, and performing brightness normalization processing on the bright field image and the dark field image; extracting lateral visual features based on the normalized image and the perceptual attention weight grid; and fusing the biased visual features with the inertial data, and solving through nonlinear optimization to obtain a carrier pose estimation result. According to the invention, the dynamic range of the binocular vision system is expanded, and the imaging and sensing capabilities of the system in an HDR scene are improved. And meanwhile, the positioning precision and robustness of the binocular visual odometer in an HDR scene are improved.

The invention discloses a wireless network resource scheduling method and a wireless network resource scheduling system, relates to the technical field of wireless network scheduling, and aims to identify an area with abnormal and prominent resource requirements by performing standardization processing on the resource requirements so as to provide a clear quantitative basis for subsequent strategy adjustment. Compared with the existing empirical or fixed threshold scheduling mechanism, the method has dynamic adaptability and cross-regional comparability, and is convenient to realize a partition and hierarchical scheduling strategy. Through construction of scheduling strategy adjustment amount, three key control dimensions in scheduling logic, namely bandwidth channel priority, transmission time slot allocation and cache scheduling sequence, are broken through, so that each control module can perform linkage adjustment based on a unified scheduling urgency degree signal. And common problems of unbalanced resource configuration and the like are avoided. Through a standardized scheduling gradient calculation mechanism, the resource urgency degrees of different regions are quantitatively compared, and the problem of wrong resource investment caused by inflexible threshold setting and incomparable regions in a traditional scheduling system is solved.

The application provides a kind of collaborative congestion control method based on attention-driven multi-path TCP sub-flow dependency modeling, MPTCP connection is established by sending end and receiving end;Subflow running data is read from MPTCP protocol stack by state monitoring module;Each subflow feature of the normalized state matrix input is weighted and fused by using self-attention layer;Action vector A is generated by Actor network t , and the effective congestion window of each subflow is used as the available sending window constraint when sending data;Action value Q is generated by Critic network;Reward R is calculated by reward calculation module t ;Form collaborative congestion control model;Realize online adaptive control;This method can realize unified decision and collaborative control of multiple subflows, can realize collaborative consistency of congestion control effect and scheduling result, and can improve the stability and comprehensive performance of multi-path transmission.

The utility model discloses an aircraft power supply connector with a replaceable insertion core, which comprises a shell, an insertion core inner frame is arranged in the shell, the insertion core inner frame is provided with the insertion core, the insertion core is composed of two connected rod bodies, the front end rod body is connected to the rear end rod body, and the rear end rod body is arranged on the insertion core inner frame; at least one insertion core is provided with a heat conduction piece and a temperature sensor, and the temperature of the insertion core is transmitted to the temperature sensor through the heat conduction piece. According to the utility model, the front end rod body can be rapidly replaced so as to adjust the length of the insertion core or match with different charging bases, thereby satisfying different use requirements, reducing the use cost of the power supply connector, improving the compatibility of the power supply connector, and prolonging the service life of the power supply connector. The temperature of the insertion core can be better transmitted to the temperature sensor through the heat conduction piece, the sensing effect of the temperature sensor is improved, and the socket is safe, reliable and high in stability.

This invention provides a visual sensor chip, comprising a pixel array composed of pixel units; wherein each pixel unit has a corresponding temporal difference path and a spatial difference path, or a corresponding intensity path, a temporal difference path, and a spatial difference path. This invention integrates the dual-path characteristics of the human visual system into existing visual sensor chips, thereby significantly improving the chip's ability to perceive spatiotemporal dynamic information and achieving high-precision, high-frame-rate, high-dynamic-range, and highly efficient and robust visual representation.

The utility model discloses a liquid cooling row and CPU liquid cooling heat abstractor, the liquid cooling row includes liquid inlet cavity, exchange cavity, liquid outlet cavity, a plurality of radiators, liquid inlet connector and liquid outlet connector, and the liquid inlet connector and liquid outlet connector are all corner connectors of 90 degree corner, and the CPU liquid cooling heat abstractor including this liquid cooling row still includes fan, liquid inlet pipe, liquid outlet pipe and liquid cooling head, and it sets up parallelly liquid outlet connector and fan, and the CPU liquid cooling heat abstractor sets up the side of liquid inlet pipe and liquid outlet pipe parallel to the liquid cooling row, under the condition of high -efficient utilization limited space, makes the case inside appearance sense remarkable promotion to reduce the bending of liquid inlet pipe and liquid outlet pipe, reduces the risk of liquid leakage, and can guarantee the heat dissipation effect.

The invention discloses a defect target detection method and a computer readable storage medium, and the method comprises the steps: obtaining a to-be-detected image of a detected object, carrying out the feature extraction of the to-be-detected image, obtaining a first feature map, predicting the position of a defect target in the to-be-detected image through the first feature map, and obtaining the prediction information of a plurality of defect target frames, the prediction information of the defect target frame comprises probability distribution of the position of each edge of the defect target frame, and the probability distribution of the position of each edge of the defect target frame comprises the probability that each edge is located at each preset discrete position in a position interval; and according to the probability distribution of the positions of the edges of the plurality of defect target frames, determining the positions of the edges of the plurality of defect target frames, and forming the plurality of defect target frames. By predicting the possibility that the edge of the defect target frame is located at different positions instead of directly predicting the exact position of the edge, the uncertainty of the edge in practice can be sensed, and the sensing and positioning capability on the defect under the subtle, fuzzy or low-contrast condition is improved.

The invention discloses a reservoir slope displacement prediction method and system, and the method comprises the following steps: obtaining reservoir slope multi-modal data of a target region, and carrying out the preprocessing of the reservoir slope multi-modal data; extracting reservoir bank slope space change characteristics according to the preprocessed reservoir slope multi-modal data, and determining reservoir bank slope displacement according to the reservoir bank slope space change characteristics; based on the pre-processed reservoir slope multi-modal data and the reservoir bank slope displacement amount, extracting spatial correlation characteristics among the modal data of the reservoir slope in the target area; and reservoir slope displacement prediction is carried out according to the spatial correlation characteristics among the modal data of the reservoir slope, and a prediction result is output. According to the method, multi-modal monitoring data are fused, space and time modeling capability is provided, modal weight can be self-adapted, deep learning reservoir slope displacement prediction can be carried out in combination with visual prediction, and high-precision, real-time and credible deformation trend prediction and risk early warning are realized.

This invention belongs to the field of medical device technology and discloses a surgical instrument system, including an operating arm and a support plate. A drive screw is mounted on the support plate; a drive element is mounted at one end of the drive screw, and a first connecting block and a second connecting block are respectively mounted on the drive screw; a rotating shaft is mounted on each of the first and second connecting blocks, and one end of the rotating shaft is rotatably connected to a surgical sheath for accommodating the operating rods; wherein the central axes of the two surgical sheaths are coplanar. This surgical instrument system can adjust the spacing between the operating rods through self-drive, and the clamping force between the operating rods is directly controlled by the doctor to provide sufficient clamping force when necessary, improving surgical stability; the clamping force is transmitted from the operating rods to the distal end, allowing for controllable handling of the surgical process; simultaneously, the coplanar central axes of the two surgical sheaths effectively prevent the two operating rods from crossing, eliminating the chopstick effect.

A method for generating contraband images based on an improved DDPM (Discretionary Data Processing Model) is disclosed. The method includes: creating a contraband dataset; constructing a contraband image generation model based on the improved DDPM and generating feature maps of contraband in different poses; determining the weights of the contraband image generation model based on the improved DDPM; and generating an image of a specific contraband. The contraband image generation method based on the improved DDPM provided by this invention has the following advantages: it reduces the number of model parameters and improves model running speed; it improves model performance, perception ability, and efficiency, better handles multi-scale features, and reduces model parameters and computational load; it better captures global contextual information in the image, thereby improving the accuracy of image segmentation and allowing the model to focus more on important regions in the image.

A method for recording a navigation trajectory, the navigation trajectory comprising a navigation trajectory of a water area mobile device, the recording method comprising: after the water area mobile device is turned on, acquiring a current navigation speed of the water area mobile device (S101); and when the current navigational speed satisfies a specified condition, recording a navigation trajectory based on the current navigational speed, the specified condition being set based on a comparison result of the current navigational speed and a preset navigational speed, the preset navigational speed being a non-zero value (S102). By setting the non-zero preset navigational speed, the appropriate threshold value is selected to eliminate the movement which is not in the threshold value range as noise, so that the impression of the navigation route is improved.

The application discloses a kind of based on live equipment of thing networking chip, including support frame, support frame is provided with rotating table, rotating table is installed with fixed plate, the inner wall of fixed plate is provided with the light supplement mechanism for live light supplement, the inner wall of support frame is provided with double-shaft motor, the present application can increase the connection strength, can indirectly improve the angle rotation stability of thing networking chip camera, can improve the live effect of the device, simultaneously can complete subsequent preparation data statistics quickly by thing networking chip camera, support cross plate can slide on ground, simultaneously in the process of support cross plate movement, the support area of the device can be increased, the stability of the device can be improved, avoid in the process of thing networking chip camera rotation, because of the equipment overall vibration caused by rotating motion, then equipment dump occurs, the image live identification stability of the device can be improved.

The present application relates to a kind of forest leaf instance segmentation method and system based on deep learning, method includes: obtaining vegetation image, vegetation image is input into leaf instance segmentation model, obtains leaf instance segmentation prediction result;Leaf instance segmentation model is trained using training set;Training set includes: vegetation original image;Feature extraction and enhancement are carried out using backbone module in leaf instance segmentation model, and adaptive spatial fusion mechanism in progressive feature pyramid network is integrated to dynamically adjust feature weight, generate dynamic fusion feature;Through the dynamic asymmetric spatial perception mechanism built-in in dynamic anomaly regression head module, the corresponding multi-source deformation feature layer of dynamic fusion feature is obtained, and the feature fusion strategy of top-down cascaded decoding module is used to optimize multi-scale feature, obtain multi-source fusion feature layer, further using multi-source fusion feature layer, generate leaf instance segmentation prediction result.The present application solves the problems of data scarcity, poor adaptability and low efficiency.

The invention discloses a power grid frequency prediction method based on LSTM and time feature attention. The power grid frequency prediction method comprises the following steps: step 1, collecting multi-dimensional time sequence feature data of a power system before and after disturbance; 2, preprocessing the multi-dimensional time sequence feature data, and extracting high-dimensional time sequence features in the feature data obtained in the step 1 by using a time convolutional neural network T-CNN; 3, inputting the high-dimensional time sequence features extracted by the time convolutional neural network T-CNN into the time-feature attention module to obtain a focusing key feature and a weighted feature sequence of a key moment; 4, taking the weighted feature sequence obtained in the step 3 as input, mining a time sequence dependency relationship through a frequency prediction model of a long short-term memory (LSTM) network, and outputting a prediction curve of the inertia center frequency of the power system; and step 5, according to the predicted frequency curve, calculating a frequency lowest point deviation and a frequency change rate, and according to a preset frequency response danger level standard, outputting a frequency safety evaluation result of the power system.

The application discloses a kind of based on double filtering cooperation's remote sensing image scene classification method and system, in the method, for the problem that existing technology is difficult to give consideration to background noise suppression and key feature edge structure preservation when processing remote sensing image, a kind of double filtering cooperation optimization module is presented.The module in this paper introduces Gaussian filter smoothing channel to suppress unstructured high-frequency background noise, while introducing Gaussian Laplace edge enhancement channel to accurately capture and strengthen key geometric structure information such as feature contour;Through feature fusion, position coding, state space model and double activation gating mechanism, the multi-scale feature map is optimized and reconstructed to generate high signal-to-noise ratio and clear structure scene representation.The module is integrated into the remote sensing image scene classification model, and trained using focal loss, which can significantly improve the classification accuracy and robustness in complex texture interference scene.

This invention proposes a multi-frame fusion infrared gas target detection method and system based on knowledge distillation, belonging to the field of computer vision technology. Multiple frames of images to be detected are input into a knowledge distillation model. First, multi-scale initial features are extracted through multiple downsampling operations of the teacher model's backbone network. Then, the initial features of the same scale are input into a corresponding bidirectional GRU to obtain the temporal fusion features of the current frame. Next, aggregated multi-scale features are obtained based on a neck feature aggregation network. Enhanced features are obtained by attention-weighting the aggregated features of the current frame at the same scale using aggregated features from other frames. During the knowledge distillation stage, the aggregated multi-scale features of the teacher model serve as a supervision signal, enabling the student model to align its feature representation with the teacher model across multiple scales, thus obtaining the gas target detection result. Therefore, by using a bidirectional GRU to model inter-frame temporal information for spaced video frames and aligning multi-scale feature representations based on knowledge distillation, the accuracy of infrared video gas target detection is improved.