CN113298006A - Novel abnormal target detection method based on brain-machine fusion cognition and decision - Google Patents
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Abstract
The invention discloses a new abnormal target detection method based on brain-machine fusion cognition and decision, which comprises the following steps: 1. and calculating the classification value of the abnormal target image through a computer vision target detection algorithm. 2. Designing an electroencephalogram signal corresponding to an abnormal target image induced by a rapid sequence vision presentation experiment, extracting and classifying the electroencephalogram signal according to characteristics, and calculating a classification value of the abnormal target image. 3. And evaluating the classification performance of the computer and the human brain on the abnormal target image, and calculating the trust weight. 4. Establishing a D-S evidence theory brain-machine fusion cognition and decision model, fusing the classification values of a computer and the human brain according to the trust weight, and calculating the brain-machine fusion classification value of the abnormal target in the image to obtain the abnormal target detection result. The method can fully fuse decision information of the computer and the human brain, reduce the decision contradiction between the computer and the human brain, improve the performance of brain-computer fusion, and effectively solve the problem of low accuracy of abnormal target detection.
Description
Technical Field
The invention belongs to the field of brain-computer interface, computer vision and intelligent information fusion cross research.
Background
Target detection is to find out the target feature expression in the image to distinguish the target from the non-target. The development of machine learning and deep learning promotes the development of computer vision target detection, and the accuracy is greatly improved. However, for abnormal targets in scenes with low visibility, such as night and snow, the imaging quality of the targets is poor, and a computer lacks sufficient identification capability, so that the requirement on accuracy cannot be met. The human beings have strong recognition capability, can acquire key visual information in the image at a glance, and can quickly detect an interested target in the image. The brain-computer interface is a communication and control technology, can enable people to directly communicate with the outside through brain activities, and can effectively realize the detection of abnormal targets by the human brain. The invention provides a method for carrying out abnormal target detection by fusing information of a computer and human brain, so that the advantages of the computer and the human brain are complemented, and the accuracy of abnormal target detection is improved.
In recent years, many studies have been made by researchers at home and abroad on abnormal target detection, and the studies mainly focus on improvement of a computer abnormal target detection model, feature extraction and classification of electroencephalograms in a target state, and fusion recognition of a computer and a human brain, and the studies have made good progress, but still have many problems: (1) the computer detection accuracy is low. The image characteristics of the abnormal target change greatly, the analysis difficulty of the image characteristics is improved, and the accuracy of computer detection is reduced. (2) The fusion cognition of the human brain and the computer has decision contradiction. At present, a fusion cognitive method of a human brain and a computer is only applied to a target detection task in a normal scene, and is not applied to an abnormal target detection task in a scene with low visibility. When the computer detects an unlearned abnormal target, false detection can occur, and when the human brain is not focused, false detection can also occur. The computer and the human brain have different false detection reasons, and the detection result of the computer and the human brain is inconsistent, so that the decision contradiction between the computer and the human brain is caused.
The novel abnormal target detection method based on brain-computer fusion cognition and decision-making provided by the invention fully fuses the detection information of the abnormal target of the computer and the human brain by establishing a brain-computer fusion cognition and decision-making model, reduces the decision contradiction between the computer and the human brain, has higher accuracy than a method when the computer and the human brain are detected independently, effectively overcomes the problems, and can be applied to the fields of automatic driving, intelligent monitoring and the like.
Disclosure of Invention
The invention provides a novel abnormal target detection method based on brain-computer fusion cognition and decision, which fuses machine intelligence and human brain intelligence and effectively improves the accuracy of abnormal target detection by utilizing the powerful calculation power of a computer and the cognitive ability of the human brain.
The basic scheme is as follows:
(1) and detecting an abnormal target image through a computer vision target detection algorithm, and calculating whether a computer classification value of a target exists in the image.
(2) And designing a rapid sequence visual presentation experiment for abnormal target detection, transmitting target information to the human brain at a high image presentation rate, inducing an electroencephalogram signal corresponding to an abnormal target image, and collecting and recording the signal through an electroencephalogram detector.
(3) The brain-computer interface technology is used for decoding the acquired brain electrical signals, performing frequency band filtering, data segmentation and baseline calibration preprocessing, time domain and space domain feature extraction and Bayesian linear discrimination classification on the brain electrical signals, and calculating whether the human brain classification value of the target exists in the image.
(4) And evaluating the classification performance of the computer and the human brain on the abnormal target image, and determining the classification value trust weight of the computer and the human brain through a confusion matrix.
(5) Establishing a D-S evidence theory brain-computer fusion cognition and decision model, taking the confidence weight and the classification value of a computer and the human brain as the input of the model, and calculating whether the brain-computer fusion classification value of the target exists in the image to obtain the result of abnormal target detection.
The invention provides a novel abnormal target detection method based on brain-machine fusion cognition and decision, and compared with the existing method, the invention has the innovation and advantages that: the decision information of the computer and the human brain for detecting the abnormal target is fused, so that the decision information is richer, and the abnormal target can be detected more comprehensively; the established D-S evidence theory brain-machine fusion cognition and decision model can fully fuse decision information of a computer and a human brain, reduce decision contradiction of the computer and the human brain, and effectively improve the accuracy of abnormal target detection.
Drawings
FIG. 1 is a flow chart of a new abnormal target detection method based on brain-machine fusion cognition and decision making according to the present invention
FIG. 2 is a schematic diagram of an experimental paradigm for rapid sequential visual inspection of abnormal targets
FIG. 3 is a comparison graph of ROC curves of a new abnormal target detection method based on brain-computer fusion cognition and decision and a method when a computer and a human brain are detected separately
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings. The method flow is shown in fig. 1, and the specific implementation mode is as follows:
(1) and (3) realizing a computer vision target detection algorithm through a training set, detecting the abnormal target image, and obtaining the classification probability of a target detection frame in the image, wherein the classification probability is a computer classification value of whether a target exists in the image or not.
(2) The abnormal target detection rapid sequence visual presentation experimental evoked task related electroencephalogram signals are designed, and an experimental paradigm is shown in fig. 2. The image stimulation with abnormal object accounts for 15% of all image stimulation, the image stimulation without abnormal object accounts for 85% of all image stimulation, all image stimulation is presented to the tested object in the form of picture flow, and the image stimulation appears at an interval of 200 ms.
(3) And acquiring the induced electroencephalogram signals, and preprocessing to obtain the electroencephalogram signals corresponding to the image stimulation. And taking the time corresponding to the image stimulation as a reference, subtracting the average value of the data of the first 50ms of the reference time from the data of the second 400ms of the reference time, and calculating to obtain the data of 400ms as the electroencephalogram signal corresponding to the image stimulation.
(4) And (4) carrying out feature extraction on the preprocessed electroencephalogram signals. The data structure of the electroencephalogram signal is DxT multiplied by N, D is the number of leads, T-400 is the number of time points, and N is the number of training set samples. Dividing data in a training set into 400N multiplied by D matrixes according to time points, respectively taking the matrixes as a target class and a non-target class, inputting the matrixes into Fisher linear decision, outputting a vector of D multiplied by 1 to each time point by the Fisher linear decision to serve as a weight of each lead, and multiplying electroencephalogram data by the weight of corresponding time to obtain data after space domain weighting. And performing principal component analysis time domain dimensionality reduction on the weighted electroencephalogram data, and dimensionality reduction on the electroencephalogram data at 400 time points to the electroencephalogram data at 6 time points.
(5) And classifying the extracted electroencephalogram characteristics through Bayesian linear discriminant analysis to obtain classification probability, namely a human brain classification value of whether a target exists in the image or not.
(6) Taking the classification value as a threshold, calculating a two-classification confusion matrix corresponding to the threshold, and calculating a classification value trust weight p through the confusion matrixtAnd pntAs shown in equation 1:
where TP is the number of samples for which the target is correctly predicted as the target class, FN is the number of samples for which the target is incorrectly predicted as the non-target class, FP is the number of samples for which the non-target is incorrectly predicted as the target class, and TN is the number of samples for which the non-target is correctly predicted as the non-target class.
(7) And establishing a D-S evidence theory brain-machine fusion cognition and decision model. Determining an identification frame with a target hypothesis T, a non-target hypothesis NT and an uncertain hypothesis I, and performing basic probability distribution on the hypotheses of the identification frame according to the trust weight values to obtain basic probability distribution of three hypotheses of a computer and a human brain respectively, as shown in formula 2:
in the formula miBasic probability distribution for computer or human brain, siAs classification value of computer or human brain, ptAnd pntAnd (3) the confidence weight value corresponding to the classification value of the computer or the human brain, wherein the computer is used when i is 1, and the human brain is used when i is 2.
The basic probability distribution of the computer and the human brain is fused through a Dempster synthesis rule, so that the fused basic probability distribution can be obtained, as shown in formula 3:
wherein m is the basic probability distribution after fusion, and N is shown in formula 4:
N＝1-m1(T)m2(NT)-m1(NT)m2(T) (4)
the classification value after brain-machine fusion is shown in formula 5:
sf＝m(T)-m(NT) (5)
(8) the experimental result of the novel abnormal target detection method based on brain-computer fusion cognition and decision is shown in fig. 3, the AUC of the novel method is higher than that of the method when the computer and the brain are separately detected, and the accuracy is higher.
Claims (4)
1. The novel abnormal target detection method based on brain-machine fusion cognition and decision-making is characterized by comprising the following steps: the detection results of the computer and the human brain on the abnormal target in the image are given through a computer vision method and a rapid sequence vision presenting brain-computer interface method respectively, a D-S evidence theory brain-computer fusion model is established to give the brain-computer fusion detection result of the abnormal target, and the accurate detection on the abnormal target is realized.
2. The novel abnormal target detection method based on brain-machine fusion cognition and decision making according to claim 1, characterized in that:
1) detecting the image through a computer vision target detection algorithm, and calculating a computer classification value of whether a target exists in the image;
2) the electroencephalogram signals corresponding to the abnormal target images are induced through rapid sequential visual presentation, the electroencephalogram signals are weighted in a space domain through a Fisher linear decision algorithm, the electroencephalogram signals are reduced through a principal component analysis method, the electroencephalogram signals are classified through Bayes, and human brain classification values of whether the targets exist in the images are calculated.
3. The novel abnormal target detection method based on brain-machine fusion cognition and decision making according to claim 1, characterized in that: and evaluating the classification performance of the computer and the human brain on the abnormal target image to obtain a two-classification confusion matrix when the classification value is used as a threshold value, and calculating the trust weight of the classification value according to the confusion matrix.
4. The novel abnormal target detection method based on brain-machine fusion cognition and decision making according to claim 1, characterized in that: establishing a D-S evidence theory brain-computer fusion cognition and decision model, performing basic probability distribution on classification values of a computer and a human brain according to the trust weight, and calculating the brain-computer fusion classification value of whether a target exists in an image through the Dempster synthesis rule fusion basic probability distribution of the computer and the human brain to obtain the result of abnormal target detection.
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