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Artificial intelligence has these problems in the security industry application
In the security industry, the advancement and future development of AI technology is unquestionable, and many companies in the industry have launched related products and solutions. Although security has always been based on artificial intelligence, but from the current point of view, the application of artificial intelligence in the market segmentation of the security industry, can not achieve the expected results, the replacement rate of AI new products is still seeking A new breakthrough. Whether it is the strength of the participating companies, or the difficulties in technology research and development and product integration, there are still many difficulties and difficulties.
1. Industry participant level
Limited by their respective technical fields and industry development, all parties involved in the research and development of AI technology in the security field have obvious advantages and disadvantages, which is also a difficult problem faced by all parties in the continuous promotion of the application of AI technology.
First, although traditional security companies have shown an attitude of actively embracing AI technology, some large security listed companies have also proposed corresponding strategies, but the time points are concentrated in the past two or three years. Mature AI products and industry solutions are relatively few, and algorithm accumulation And the integration time with the industry is still short. According to the current market reaction, traditional security enterprise AI products are still limited to face recognition, vehicle identification and the application of the corresponding big data platform.
Secondly, some AI algorithm companies began to turn their perspectives into security field from the past four or five years ago, and based on their own accumulated advantages in algorithms, they launched corresponding AI security products and solutions. However, in terms of hardware manufacturing, industry accumulation and channel expansion, algorithm companies have a big gap with traditional security manufacturing enterprises, especially in the application of segmentation, and need to be further improved.
Finally, the security SMEs at the bottom have neither financial strength nor R&D in the algorithm field, nor have the ability to obtain big data support through establishing cooperative relationships with the local business departments, but only have experience in industry application in sub-divisions. This is also a series of difficult problems that security SMEs face in the AI ​​era.
2. Technical level
At present, the application of artificial intelligence technology in the security industry has shown a prosperous situation, but the current application is only shallow, and the technology is still immature. In some scene applications, artificial intelligence can not achieve ideal landing results. . For example, AI has poor environmental adaptability in the subdivision field. At present, the recognition rate is relatively high in view of the relative standardization of the vehicle and road environment, but the accurate recognition of the face is easily exposed to insufficient illumination, blurred images, and the target size is too small. Or environmental influences such as mutual occlusion, thus affecting the recognition accuracy.
In addition, data resources are scattered, and the openness and sharing degree of monitoring data in the security field is relatively low. It is difficult to carry out cross-fusion analysis of multi-dimensional data, which makes the artificial intelligence analysis lack effective data support, and also affects the accuracy rate. At the same time, different scenarios are limited in understanding. Due to the lack of effective professional domain experience and knowledge, the understanding of video content is weak. The current intelligent analysis is mostly single-scenario target detection and behavior analysis, and rarely involves large-scale scenarios. Correlation behavior analysis makes it difficult to use for abnormal behavior analysis and risk prediction.
3. Landing application level
As early as 2012, after deep learning was widely applied, some AI algorithm companies turned their perspectives into security and developed AI security products based on artificial intelligence or deep learning. From the perspective of product lines, it is mainly divided into portrait recognition and control system, video structured analysis system, vehicle big data platform, police big data platform, and AR real-life command system. However, in terms of hardware manufacturing, industry accumulation and channel expansion, there is a big gap between algorithm companies and traditional security manufacturing enterprises, especially in the application of segmentation, which needs to be further improved.