Deep learning algorithm rises background
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May 06 11:29:53, 2023
Since the advantage of deep learning is so obvious, and this method was proposed in the late 1980s, why did it not begin to rise and surpass humanity until the last two years? To sum up, there are three major factors that jointly determine that deep learning has only begun to break out in recent years: data size, computing power, and network architecture.
First, big data is an important road to deep learning success. In today's Internet age, the accumulation of data volume is explosive, and more and more areas are continuously accumulating increasingly rich application data, which is crucial for the further development and application of deep learning. However, the collection of big data is costly, and the cost of labeling has already begun to rise. The quality of the sample directly determines the accuracy of the model, so only companies with certain technical strength can continue to invest in research. In the field of security, companies with independent research and development strengths such as Hikvision and years of deep pride in the security industry have used large amounts of video and image data from real-life video surveillance scenes as training sample databases. The data volume is large and the quality is better. More than 100 people passed. The team's data group tags the video images and accumulates tens of millions of sample data. Under the continuous training of these large-volume and good-quality samples, the system recognizes people, vehicles, and objects in the security monitoring environment. The model will be more and more accurate.
Second, high-performance hardware platform computing is engine power. Deep learning model requires a large number of samples, which can not avoid a large number of calculations, and previous hardware devices are not enough to train complex hundreds of deep learning models. In 2011 Google DeepMind used 1,000 machines and 16000 CPUs to handle about 1 billion neurons in depth. Now, with just a few GPUs, we can do the same calculations and iterate faster. Therefore, the rapid development of high-performance hardware platforms such as GPUs, supercomputers, and cloud computing has made deep learning possible. The powerful computing capabilities have enabled deep learning algorithms to quickly verify and accumulate more experience for model correction, further improving model accuracy. .
Finally, the structural innovation of the algorithmic network is a highly efficient fuel. Through continuous optimization of the deep learning algorithm, the target can be better identified. In the field of security, for some complex scenes, such as face recognition, lighting, angles, poses, expressions, accessories, resolution, etc. will affect the recognition accuracy, which requires the algorithm model has a stronger generalization ability, deep learning model needs further optimization. The deeper the level of deep learning algorithm is, the better the performance will be. Currently, Hikvision's deep learning algorithm level has reached 200 levels and is in the leading position in the industry. In the 2016 ImageNet contest, the Hikvision Institute ranked first in Faster R-CNN deep learning target detection algorithm, leading the rankings in the first place in the nearly one-year second place Microsoft 4.1 points, set a new record. In addition, Hikvision ranked first in the world in terms of vehicle inspection and heading direction assessment technology in KITTI assessment, and ranked first in the world for results of multi-target tracking technology in MOT Challenge evaluation.
In general, with the rapid accumulation of security big data, the rapid development of large-scale parallel computing, and the continuous emergence of more optimized algorithms, the conditions for the rise of deep learning algorithms cannot be ignored.